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Job Description Role: Data Engineer - Real-Time & Cloud Platforms Location :- Nashville, TN ( Onsite from day 1 ) Duration: Long Term Job Summary As a Consulting Data Engineer, you will architect, build, and optimize the next generation of our real-time healthcare data platforms and event-driven architectures. You will play a foundational role in moving our clinical, operational, and financial data away from legacy batch processing toward high-throughput, near-zero-latency streaming pipelines. In this role, you will champion modern data engineering practices, design highly scalable cloud-native pipelines, and mentor a growing team of engineers. You will bridge the gap between complex electronic health records (EHR) and data consumer-enabling data scientists, software engineers, and clinical analysts to securely access, stream, and query compliant data at scale to improve patient outcomes and operational efficiency. Core Responsibilities • Architect Real-Time Pipelines: Design, implement, and monitor high-throughput, fault-tolerant streaming pipelines that ingest and process millions of concurrent healthcare data events (e.g., live telemetry, HL7 feeds, ADT messages). • Scale Google Cloud Infrastructure: Build and maintain scalable, secure, and cost-effective cloud data infrastructure using Google Cloud healthcare-native tools and Infrastructure as Code (IaC). • Enforce Healthcare Compliance & Security: Uphold strict data security protocols (HIPAA, HITRUST, SOC2) by building robust data lakehouse architectures that enforce PHI/PII masking, role-based access control (RBAC), and encryption at rest and in transit. • Enable Self-Service Data & Interoperability: Build out streaming data ecosystems that support modern healthcare interoperability, managing real-time data catalogs and schema registries. • Promote Code Excellence: Write modular, highly reusable, and performance-tested code; lead code reviews and establish standardized CI/CD automated deployment workflows. • Optimize Data Reliability: Set and champion strict SLAs for clinical data delivery, establish automated data-quality testing, and implement proactive system monitoring and alerting. • Collaborate & Mentor: Work closely with Data Architects, Clinical Product Managers, and AI/ML teams to translate healthcare requirements into technical blueprints while mentoring mid-level engineers. Required Qualifications Experience & Education • 13+ years of experience in dedicated data engineering, data architecture, or distributed systems software engineering roles. • 4+ years of experience specifically within the healthcare domain (payer, provider, digital health, or health tech). • Google Cloud Certified Professional Data Engineer (or equivalent proven expert-level architecture experience on GCP). True Streaming Focus • Advanced Event-Driven Architecture: Mastery of high-volume streaming technologies including Apache Kafka and Google Cloud Pub/Sub for decoupled, real-time message routing. • Stream Processing: Deep hands-on experience with Apache Flink or Google Cloud Dataflow (Apache Beam) for real-time streaming, complex event processing (CEP), windowing, and streaming data transformations. Healthcare-Specific Tech Stack • Interoperability Standards: Deep expertise working with FHIR (Fast Healthcare Interoperability Resources) JSON structures, HL7 v2/v3 messaging, and DICOM imaging standard streams. • GCP Healthcare Tools: Hands-on experience implementing and scaling the Google Cloud Healthcare API (specifically FHIR stores and HL7v2 stores). • EHR Integration: Experience integrating streaming pipelines with major EHR systems (such as Epic, Cerner, or Meditech). Cloud Ecosystem & Modern Tooling • Google Cloud Ecosystem: Technical mastery of BigQuery (including partitioning, clustering, and streaming inserts), Cloud Storage, Dataproc, and Cloud Composer (Apache Airflow). • Programming Languages: Proficiency in Python, Scala, or Java, with a strong focus on writing optimized code for distributed computing platforms (like Spark or Beam). • Modern Data Stack & Modeling: Mastery of modern analytics engineering frameworks (such as dbt), dimensional data modeling, and lakehouse architectures (e.g., Iceberg, Delta Lake). • DevOps & Automation: Robust experience building automated CI/CD pipelines (GitHub Actions, GitLab CI) and managing infrastructure using Terraform.

Job Description Role Overview The Project Manager will join our team in Pittsburgh to lead client-facing engagements within an IT consulting environment. This role involves managing high-impact project teams in areas such as AI, software development, data, and automation domains. You will serve as the primary point of contact for clients, ensuring the successful delivery of complex consulting projects, fostering team performance, and driving measurable business outcomes through expert project management and coordination. Key Responsibilities Lead end-to-end management of client-facing consulting engagements from initiation to closure. Direct and motivate multidisciplinary project teams (developers, data scientists, AI engineers) to deliver high-quality solutions. Serve as the primary liaison between client stakeholders and our internal technical teams to manage expectations, scope, and project timelines. Monitor and report on project health, budget utilization, and resource allocation to both internal leadership and client stakeholders. Facilitate project planning, risk management, issue resolution, and status reporting in a non-agile delivery framework. Ensure all project deliverables meet quality standards and align with the client's strategic business requirements. Provide mentorship and guidance to team members to maintain high levels of engagement and productivity. Drive continuous improvement in project management processes and delivery methodologies across the firm. Translate complex technical project requirements into actionable tasks and clear documentation. Maintain strong client relationships by demonstrating a customer-focused, proactive approach to problem-solving. Required Qualifications 8+ years of professional project management experience within an IT consulting organization. Current Scrum Master Certification (CSM or equivalent). Demonstrated experience managing projects in the AI, software development, data, or automation domains. Local presence in or willingness to be based in Pittsburgh, PA. Expertise in managing project lifecycles using non-agile (waterfall or hybrid) delivery methodologies. Proven track record of managing paid client engagements and delivering complex technical solutions. Excellent verbal and written communication skills with the ability to influence both technical and non-technical stakeholders. Strong critical thinking and analytical problem-solving abilities. Expertise in risk identification, mitigation, and contingency planning. Bachelor's degree in Business, Computer Science, Information Technology, or a related field. Exceptional organizational skills with a strong attention to detail and ability to balance multiple priorities. Self-motivated, adaptable, and a strong ownership mindset regarding project outcomes. Preferred Qualifications Specific experience managing AI-focused project teams or complex software application development lifecycles. Background in professional services or management consulting firms. Experience in stakeholder management at the VP or Director level. Advanced project management certifications (e.g., PMP). Location: Pittsburgh, PA About Techstra Solutions Techstra Solutions is a certified woman-owned (WBENC) management consulting firm specializing in strategy, technology, and implementation services for large organizations undergoing digital and talent transformation. Our experienced team partners with clients to co-create innovative solutions in applications, data, AI, and automation that accelerate measurable, sustainable change. From advisory consulting through technical execution, we are dedicated to driving world-class business solutions that fit your strategic requirements and deliver results. For more information:

Pay Range: $60.00hr - $65.00hr Job Overview: The Azure Data Architecture Lead is responsible for providing architectural leadership in designing, implementing, and optimizing large-scale data solutions using Azure Data Factory and related Azure services. This role drives enterprise-scale data initiatives, ensuring solutions are robust, scalable, and aligned with industry best practices. The position plays a critical part in shaping data strategy, enforcing governance, and fostering innovation to support organizational and client objectives. Key Responsibilities: Architect end-to-end data integration and transformation solutions using Azure Data Factory, ensuring scalability, reliability, and adherence to enterprise standards. Design and oversee advanced analytics platforms leveraging Azure Databricks, Azure Synapse Analytics, and Azure ML to enable data-driven decision-making. Define and implement DevOps practices for data pipelines in Azure, ensuring automated deployments, monitoring, and operational excellence. Lead architectural reviews and enforce governance and compliance measures across Azure data solutions, maintaining alignment with industry and organizational standards. Collaborate with business stakeholders to gather requirements and translate them into innovative, scalable Azure-based data architectures. Mentor and develop technical teams on Azure data technologies, ensuring skill advancement and risk mitigation in solution delivery. Author whitepapers, contribute to industry forums, and drive intellectual property initiatives to establish thought leadership in Azure data architecture. Skill Requirements: Architectural leadership in Azure Data Factory with expert-level proficiency in designing complex data integration solutions. Advanced expertise in Azure Databricks, Azure Synapse Analytics, and Azure ML for large-scale analytics and machine learning workloads. In-depth knowledge of DevOps practices for Azure data services, including CI/CD, monitoring, and automation. Solid understanding of data governance, security, and compliance frameworks within Azure environments. Excellent ability to translate business requirements into scalable Azure data architectures. Strong mentoring and leadership skills to guide technical teams in Azure data technologies. Other Requirements: Microsoft Certified: Azure Solutions Architect Expert (Recommended). Microsoft Certified: Azure Data Engineer Associate (Optional but valuable). Microsoft Certified: Azure Data Scientist Associate (Optional but valuable). Benefits: Our Benefits Include: Medical, Dental, and Vision Insurance 401(k) Retirement Plan Health Savings Account (HSA) Disability Insurance (Short-Term and Long-Term) Life and AD&D Insurance Paid Sick Leave (where required by applicable state or local law) Supplemental Insurance Plans Identity Theft Protection Pet Insurance Employee Wellness Programs Employee Assistance Program (EAP) Career Growth and Professional Development Opportunities Disclaimer: Benefits eligibility, accrual rates, and usage limits may vary based on employment status, length of service, and work location. Paid Sick Leave is provided in strict accordance with applicable state and municipal mandates. Cynet Systems Inc. reserves the right to modify, amend, or terminate any benefit plans at any time in accordance with applicable laws. About Cynet Systems Founded in 2010 and headquartered in the Washington, DC metro area, Cynet Systems Inc. is a leading technology staffing and workforce solutions company serving Fortune 500 companies, government agencies, and enterprise organizations across the United States and Canada. We deliver agile, scalable talent solutions across IT, engineering, life sciences, clinical, and professional staffing, powered by a high-performing recruitment engine operating across North America and Asia. As a nationally and locally certified Minority Business Enterprise (MBE), Cynet Systems is committed to helping organizations build high-performing teams while empowering professionals to grow rewarding careers. Our organization is certified to ISO 9001, ISO 14001, ISO 27001, and SOC 2 Type II standards, reflecting our commitment to quality, security, operational excellence, and customer success.

McKesson is an impact-driven, Fortune 10 company that touches virtually every aspect of healthcare. We are known for delivering insights, products, and services that make quality care more accessible and affordable. Here, we focus on the health, happiness, and well-being of you and those we serve - we care. What you do at McKesson matters. We foster a culture where you can grow, make an impact, and are empowered to bring new ideas. Together, we thrive as we shape the future of health for patients, our communities, and our people. If you want to be part of tomorrow's health today, we want to hear from you. Role Overview The Lead Business Strategic Insights role partners closely with leaders across Commercial Strategy & Innovation (CSI) to develop data-driven solutions that accelerate business growth, enhance customer outcomes, and bring innovative products to market. This individual will leverage advanced analytics, artificial intelligence, machine learning, and business insights to solve complex challenges, uncover opportunities, and drive strategic decision-making across the organization. This role will participate throughout the full solution lifecycle, from ideation and proof of concept through production deployment and business adoption. Success in this position requires a combination of analytical expertise, business acumen, technical proficiency, and stakeholder influence. Key Responsibilities Accelerate AI-enabled decision-making by partnering with data governance, product, and technology teams to leverage governed data assets, semantic layers, and AI/LLM technologies that enable business users to access trusted insights more efficiently. Design and develop heuristics, optimization models, and advanced analytics solutions across customer, product, and operational domains to improve business outcomes. Utilize qualitative and quantitative data to identify opportunities, develop hypotheses, pilot proof-of-concept solutions, and scale successful models across the enterprise. Apply data science and analytical methodologies to identify market trends, process improvement opportunities, strategic synergies, and actionable business recommendations. Support digital data acquisition and integration strategies while building scalable data pipelines that complement existing enterprise data assets. Leverage CSI's data infrastructure to develop next-generation analytical models focused on customer value creation, margin expansion, and operational efficiencies. Drive experimentation and causal learning through pilots, A/B testing, and other measurement methodologies to quantify business impact and improve decision-making. Break down complex business challenges into scalable workstreams and collaborate across functional teams to drive solution adoption and implementation. Develop and deploy scalable Python-based APIs and data products that integrate with enterprise applications and business systems. Build and scale AI agents that enable business users to interact with data using natural language, uncover insights, identify opportunities, and make faster, more informed decisions. Present findings, recommendations, and business cases to senior leaders and cross-functional stakeholders. Minimum Requirements Bachelor's degree in Business, Data Science, Computer Science, Statistics, Engineering, Mathematics, or a related field, or equivalent experience. Typically requires 7+ years of relevant professional experience in analytics, data science, business intelligence, strategy, or a related discipline. Critical Skills 5+ years of experience in analytics, data science, business intelligence, or a related field. Proven experience leveraging analytics to drive measurable business value and strategic outcomes. Strong understanding of software engineering principles and API development used to commercialize analytical products and solutions. Advanced communication, presentation, and stakeholder management skills. Hands-on experience with Python, including libraries such as Pandas, Scikit-learn, NumPy, and related tools. Advanced SQL skills with experience querying and manipulating large, complex datasets. Strong understanding of machine learning techniques, including clustering, regression, anomaly detection, forecasting, classification, deep learning, and time series analysis. Experience working with cloud-based data platforms and modern analytics ecosystems. Experience building, deploying, or leveraging AI and generative AI solutions within business environments. Preferred Qualifications Experience working in healthcare, distribution, pharmaceutical, or supply chain environments. Experience with experimental design, causal inference, and business impact measurement. Familiarity with modern AI frameworks, large language models (LLMs), and agentic AI solutions. Experience building scalable data products and deploying machine learning models into production environments. Demonstrated success influencing business decisions through analytics and strategic insights. Additional Skills Passion for using data to solve complex business problems. Strong business acumen and strategic thinking capabilities. Ability to balance technical depth with business priorities. Self-starter with a continuous improvement mindset. Strong collaboration and relationship-building skills across functions and leadership levels. This role is a hybrid position. The selected candidate is expected to work on-site at our Las Colinas office a minimum of two (2) days per week, with the remaining days worked remotely. Specific in-office days may be designated based on team needs and business priorities. We are proud to offer a competitive compensation package at McKesson as part of our Total Rewards. This is determined by several factors, including performance, experience and skills, equity, regular job market evaluations, and geographical markets. The pay range shown below is aligned with McKesson's pay philosophy, and pay will always be compliant with any applicable regulations. In addition to base pay, other compensation, such as an annual bonus or long-term incentive opportunities may be offered. For more information regarding benefits at McKesson, please click here. Our Base Pay Range for this position $136,300 - $227,100 McKesson has become aware of online recruiting-related scams in which individuals who are not affiliated with or authorized by McKesson are using McKesson's (or affiliated entities, like CoverMyMeds or RxCrossroads) name in fraudulent emails, job postings or social media messages. In light of these scams, please bear the following in mind: McKesson Talent Advisors will never solicit money or credit card information in connection with a McKesson job application. McKesson Talent Advisors do not communicate with candidates via online chatrooms or using email accounts such as Gmail or Hotmail. Note that McKesson does rely on a virtual assistant (Gia) for certain recruiting-related communications with candidates. McKesson job postings are posted on our career site: . McKesson is an Equal Opportunity Employer McKesson provides equal employment opportunities to applicants and employees, without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability, age, genetic information, or any other legally protected category. For additional information on McKesson's full Equal Employment Opportunity policies, visit our Equal Employment Opportunity page. McKesson is committed to being an Equal Employment Opportunity Employer and offers opportunities to all job seekers including job seekers with disabilities. If you need a reasonable accommodation to assist with your job search or application for employment, please contact us by sending an email to (United States) or (Canada) . Resumes or CVs submitted to this email box will not be accepted. Join us at McKesson!

JOB SUMMARY: NORC at the University of Chicago is seeking a qualified Data Scientist I to join the Methodology and Quantitative Social Sciences department and support innovative research and analytics. The Data Scientist I works collaboratively with methodologists, researchers, statisticians, software developers, and subject matter experts throughout the research lifecycle, including study design, data collection, quality assessment, quantitative analysis, and dissemination of results. This role combines strong foundations in data management, research methodology, and computational social science to develop methodological and data science solutions that improve research, operational efficiency, and decision-making. The Data Scientist I will contribute to projects involving structured and unstructured data, survey data, administrative records, commercial data, text data, and other novel data sources. Responsibilities may include developing data pipelines, implementing data management approaches, maintaining analytical workflows, conducting analysis and creating data visualizations, and assisting in the writing and dissemination of results. The ideal candidate possesses strong social science research and data expertise, written and communication skills, R programming expertise, experience building reproducible analytical workflows, and an interest in applying advanced methods to solve complex research and business problems. Data Scientists are expected to manage multiple assignments, contribute to projects and proposals, stay current with emerging analytic tools, and work effectively in a collaborative, interdisciplinary environment. Location: This is a hybrid role based in our Chicago Loop office, with a minimum of six days per month in the office. Qualified applicants must be eligible to work in the U.S. We regret that we are unable to offer visa sponsorship for this position. DEPARTMENT: Methodology and Quantitative Social Sciences (MQSS) The Methodology and Quantitative Social Sciences (MQSS) department implements state-of-the-art methodologies and develops innovations to deliver reliable data and rigorous analysis to guide critical programmatic, business and policy decisions for NORC clients. The department provides leadership throughout the project lifecycle on study design, data collection, assessment of data quality, quantitative analysis, and dissemination of results. The Methodological and Quantitative Social Sciences department also conducts its own research and is a leader in designing and implementing rigorous, efficient methods for gathering, evaluating, and analyzing data from primary and secondary sources. The department provides expertise and leads NORC strategy on the use of a broad range of methods and techniques, including research and experimental design, recruitment and retention, instrument design and testing, assessing data quality, evaluating measurement properties of new measures, causal inference methods, machine learning, analysis of clustered data, data visualization, use of novel data sources and technologies to improve data gathering, and building AI solutions that support NORC's research. The department collaborates with the Statistics and Data Science Department on areas of synergy and intersection and with all NORC subject matter departments, in addition to leading its own projects. RESPONSIBILITIES: Apply computational and machine learning methods to support survey research, evaluation studies, and other social science research initiatives Contribute to and review the methodological/analytical sections of reports, tables/graphics, presentations, dashboards, and other deliverables Assist with proposal and business development activities related to methodological, analytic, and/or study design tasks. Establish reproducible, quality assured analytic workflows; implement version control (Git), environment management, peer code review, automated testing, and validation checks. Develop robust data pipelines that integrate, transform, and analyze structured and unstructured data from multiple sources to support research and operational objectives. Design and implement data products, dashboards, applications, and automated tools that enable data-driven decision making. Write and implement R programs to extract/manipulate data, link complex datasets, and execute machine learning and statistical analyses. Present research papers at professional conferences and submit articles for publication in journals. Work with relational databases, cloud platforms, and large-scale computing environments to manage and analyze complex datasets efficiently. Uphold data disclosure limitation and data privacy best practices. Perform other duties as assigned. REQUIRED SKILLS: Bachelor's degree in computational social science, data science, computer science or a related quantitative field with social science emphasis. At least 4 years of relevant experience (graduate research, internships, and applied professional experience may be considered). Demonstrated proficiency developing production-quality code in R. Strong foundation in areas of social science research, analytic methods, data engineering, machine learning, and scientific methods of inquiry. Must be detail-oriented and quality-focused, with demonstrated written and oral communication skills Strong interpersonal communication, analytic capability, technical writing, problem identification and resolution, and task management Ability to work on multiple projects simultaneously, effectively organize and manage time, including ability to set priorities to meet all project, proposal, and administrative needs in a timely fashion Experience developing software, data pipelines, or analytical applications. Experience conducting statistical analysis and machine learning using real-world datasets. Experience working with Git and collaborative development workflows. Enthusiasm for teamwork and collaboration across disciplinary boundaries. Preferred Skills: Demonstrated experience programming in Python Experience analyzing survey, administrative, or other large-scale observational datasets, and/or working with longitudinal survey data. Experience with interactive data visualization and mapping libraries such as R Shiny, D3.js or other JavaScript data visualization libraries and HTML/CSS. Experience with large-scale data platforms such as Databricks, Spark, Hadoop, or Hive. Experience developing, evaluating, or deploying machine learning and AI solutions, including work with large language models, and cloud environments (e.g., AWS, Azure, GCP). SALARY AND BENEFITS: The pay range for this position is $90,000-$100,000. This position is classified as regular. Regular staff are eligible for NORC's comprehensive benefits program. Benefits include, but are not limited to: Generously subsidized health insurance, effective on the first day of employment Dental and vision insurance A defined contribution retirement program, along with a separate voluntary 403(b) retirement program Group life insurance, long-term and short-term disability insurance Benefits that promote work/life balance, including generous paid time off, holidays; paid parental leave, bereavement leave, tuition assistance, and an Employee Assistance Program (EAP). NORC is committed to equity and transparency in its pay practices. We publish salary ranges and benefit information for every job. The listed hiring range reflects what we, in good faith, expect to pay at the time of posting, though actual compensation may vary and may be adjusted over time. A candidate's placement within the range depends on factors such as competencies, education, qualifications, experience, skills, performance, and organizational needs. WHAT WE DO: NORC at the University of Chicago is an objective, non-partisan research institution that delivers reliable data and rigorous analysis to guide critical programmatic, business, and policy decisions. Since 1941, our teams have conducted groundbreaking studies, created and applied innovative methods and tools, and advanced principles of scientific integrity and collaboration. Today, government, corporate, and nonprofit clients around the world partner with us to transform increasingly complex information into useful knowledge. WHO WE ARE: For over 80 years, NORC has evolved in many ways, moving the needle with research methods, technical applications and groundbreaking research findings. But our tradition of excellence, passion for innovation, and commitment to collegiality have remained constant components of who we are as a brand, and who each of us is as a member of the NORC team. With world-class benefits, a business casual environment, and an emphasis on continuous learning, NORC is a place where people join for the stellar research and analysis work for which we're known, and stay for the relationships they form with their colleagues who take pride in the impact their work is making on a global scale. EEO STATEMENT: NORC is an equal opportunity employer. NORC evaluates qualified applicants without regard to race, color, religion, sex, gender, national origin, disability, status as a protected veteran, sexual orientation, and other legally protected characteristics.

Pay Range: $50.00hr - $55.00hr Job Overview: As a Consulting Data Engineer, the successful candidate will architect, build, and optimize the next generation of real-time healthcare data platforms and event-driven architectures. The candidate will play a foundational role in moving clinical, operational, and financial data away from legacy batch processing toward high-throughput, near-zero-latency streaming pipelines. In this role, the candidate will champion modern data engineering practices, design highly scalable cloud-native pipelines, and mentor a growing team of engineers. The candidate will bridge the gap between complex electronic health records (EHR) and data consumers-enabling data scientists, software engineers, and clinical analysts to securely access, stream, and query compliant data at scale to improve patient outcomes and operational efficiency. Core Responsibilities: Architect Real-Time Pipelines: Design, implement, and monitor high-throughput, fault-tolerant streaming pipelines that ingest and process millions of concurrent healthcare data events (e.g., live telemetry, HL7 feeds, ADT messages). Scale Google Cloud Infrastructure: Build and maintain scalable, secure, and cost-effective cloud data infrastructure using Google Cloud healthcare-native tools and Infrastructure as Code (IaC). Enforce Healthcare Compliance & Security: Uphold strict data security protocols (HIPAA, HITRUST, SOC2) by building robust data lakehouse architectures that enforce PHI/PII masking, role-based access control (RBAC), and encryption at rest and in transit. Enable Self-Service Data & Interoperability: Build out streaming data ecosystems that support modern healthcare interoperability, managing real-time data catalogs and schema registries. Promote Code Excellence: Write modular, highly reusable, and performance-tested code; lead code reviews and establish standardized CI/CD automated deployment workflows. Optimize Data Reliability: Set and champion strict SLAs for clinical data delivery, establish automated data-quality testing, and implement proactive system monitoring and alerting. Collaborate & Mentor: Work closely with Data Architects, Clinical Product Managers, and AI/ML teams to translate healthcare requirements into technical blueprints while mentoring mid-level engineers. Required Qualifications: 13+ years of experience in dedicated data engineering, data architecture, or distributed systems software engineering roles. 4+ years of experience specifically within the healthcare domain (payer, provider, digital health, or health tech). Google Cloud Certified Professional Data Engineer (or equivalent proven expert-level architecture experience on GCP). Advanced Event-Driven Architecture: Mastery of high-volume streaming technologies including Apache Kafka and Google Cloud Pub/Sub for decoupled, real-time message routing. Stream Processing: Deep hands-on experience with Apache Flink or Google Cloud Dataflow (Apache Beam) for real-time streaming, complex event processing (CEP), windowing, and streaming data transformations. Interoperability Standards: Deep expertise working with FHIR (Fast Healthcare Interoperability Resources) JSON structures, HL7 v2/v3 messaging, and DICOM imaging standard streams. GCP Healthcare Tools: Hands-on experience implementing and scaling the Google Cloud Healthcare API (specifically FHIR stores and HL7v2 stores). EHR Integration: Experience integrating streaming pipelines with major EHR systems (such as Epic, Cerner, or Meditech). Google Cloud Ecosystem: Technical mastery of BigQuery (including partitioning, clustering, and streaming inserts), Cloud Storage, Dataproc, and Cloud Composer (Apache Airflow). Programming Languages: Proficiency in Python, Scala, or Java, with a strong focus on writing optimized code for distributed computing platforms (like Spark or Beam). Modern Data Stack & Modeling: Mastery of modern analytics engineering frameworks (such as dbt), dimensional data modeling, and lakehouse architectures (e.g., Iceberg, Delta Lake). DevOps & Automation: Robust experience building automated CI/CD pipelines (GitHub Actions, GitLab CI) and managing infrastructure using Terraform. Benefits: Our Benefits Include: Medical, Dental, and Vision Insurance 401(k) Retirement Plan Health Savings Account (HSA) Disability Insurance (Short-Term and Long-Term) Life and AD&D Insurance Paid Sick Leave (where required by applicable state or local law) Supplemental Insurance Plans Identity Theft Protection Pet Insurance Employee Wellness Programs Employee Assistance Program (EAP) Career Growth and Professional Development Opportunities Disclaimer: Benefits eligibility, accrual rates, and usage limits may vary based on employment status, length of service, and work location. Paid Sick Leave is provided in strict accordance with applicable state and municipal mandates. Cynet Systems Inc. reserves the right to modify, amend, or terminate any benefit plans at any time in accordance with applicable laws. About Cynet Systems Founded in 2010 and headquartered in the Washington, DC metro area, Cynet Systems Inc. is a leading technology staffing and workforce solutions company serving Fortune 500 companies, government agencies, and enterprise organizations across the United States and Canada. We deliver agile, scalable talent solutions across IT, engineering, life sciences, clinical, and professional staffing, powered by a high-performing recruitment engine operating across North America and Asia. As a nationally and locally certified Minority Business Enterprise (MBE), Cynet Systems is committed to helping organizations build high-performing teams while empowering professionals to grow rewarding careers. Our organization is certified to ISO 9001, ISO 14001, ISO 27001, and SOC 2 Type II standards, reflecting our commitment to quality, security, operational excellence, and customer success.

Job Description Introduction: Maryland Benefits (MD Benefits) is a dynamic, cloud-based platform. This enterprise-wide digital service allows organizations to build, test, host, operate, and integrate mission-driven applications, data, and emerging technologies. MD Benefits offers cloud-based Platform-as-a-Service (PaaS) capabilities, a shared data architecture, and product development services, all developed by the State of Maryland to help multiple agencies deliver and manage health, human, and social service benefits and programs. On July 1, 2025, the operation of the MD THINK shared platform and statewide applications will transition from the Department of Human Services (DHS) to the Department of Information Technology (DoIT). Minimum Qualifications: Bachelor's degree in Computer Science, Information Technology, or a related field. (Five years of relevant professional experience may be substituted for a degree). 5+ years of experience in a technical support or operations role, with a focus on cloud technologies. Proven experience with AWS services , including EC2, S3, VPC, and IAM. Strong understanding of networking concepts , including TCP/IP, DNS, and load balancing. Experience with containerization technologies such as Docker and Kubernetes. Excellent troubleshooting and problem-solving skills. Strong communication and interpersonal skills. Ability to work effectively in a fast-paced, high-pressure environment. AI/ML Ops Engineer Position Description: The AI/ML Ops Engineer is responsible for operationalizing and managing the entire lifecycle of artificial intelligence and machine learning models on the Maryland Benefits Platform. This specialized role bridges data science and platform operations, ensuring ML models are deployed, monitored, and maintained with the same rigor and reliability as traditional software applications. The engineer will build and manage the infrastructure and automation pipelines necessary for robust, scalable, and secure machine learning operations (MLOps), enabling the platform to leverage data-driven insights effectively. Responsibilities: ML Pipeline Automation: Design, build, and maintain CI/CD/CT (Continuous Integration/Continuous Delivery/Continuous Training) pipelines for machine learning models. Automate all stages of the ML lifecycle, including data preprocessing, model training, validation, deployment, and monitoring. Model Deployment & Serving: Deploy trained ML models as scalable, high-availability API services in a production environment, leveraging containerization (Docker) and orchestration platforms (Kubernetes/EKS). Manage deployment strategies such as A/B testing, canary, and blue-green deployments. Infrastructure Management: Provision and manage the cloud infrastructure required for both model training and real-time inference, including GPU-accelerated compute instances and scalable data storage solutions on AWS. Model Monitoring & Maintenance: Implement comprehensive monitoring solutions to track model performance, data drift, and concept drift in production. Establish automated alerts and retraining triggers to ensure models remain accurate and reliable over time. Collaboration & Tooling: Work closely with data scientists to understand model requirements and assist in packaging models for production. Collaborate with PaaS and Cloud Engineering teams to integrate MLOps tooling with the existing platform infrastructure. Manage and support the MLOps toolchain, which may include platforms like Kubeflow, MLflow, or AWS SageMaker.

Job Description No C2C, Strictly on W2 only Should be local to Tampa Bay Area for hybrid onsite model Duties: The Lead Data Scientist supports development of advanced decision support systems by employing advanced techniques from data analytics (including statistical analysis) and machine learning, particularly including NLP. The successful candidate will join an innovative and energetic team that develops capabilities that improve the performance of our business units and make them more efficient. This is a hands-on role in which the Data Scientist is expected to carry out a project from start to finish. This job requires working at our headquarters in St. Petersburg, FL. Skills: Excellent communication skills, with the ability to explain technical concepts to a non-technical audience Strong knowledge of Machine Learning techniques, including NLP Knowledge of statistics, including hypothesis testing Strong Python skills Proficient in SQL Comfortable with Unix scripting Experience building models that actually delivered value to a business Experience deploying systems to a production environment Experience working in an Agile environment Working knowledge of "big data" technology Education: Minimum of a Bachelor's degree and six (6) years of relevant experience or combination of education, training and experience.

Job Description Overview: At Zebra, we are a community of innovators who come together to create new ways of working. United by curiosity and a culture of caring, we develop smart solutions that anticipate our customer's and partner's needs and solve their challenges. Being part of Zebra Nation means you are seen, heard, valued, and respected. Drawing from our unique perspectives, we collaborate to deliver on our purpose. Here you are part of a team pushing boundaries today to redefine the work of tomorrow for organizations, their employees, and those they serve. You'll have opportunities to learn and lead in a forward-thinking environment, defining your path to a fulfilling career while channeling your skills toward causes you care about-locally and globally. Come make an impact every day at Zebra. What We're Looking For: Designs, develops, programs and implements Machine Learning Models (Clustering, Classification or Regressions models), Implements Artificial/Augmented Intelligence systems, Performs Statistical Modelling and Measurements by applying data engineering, feature engineering, statistical methods, ML modelling and AI techniques on structured, unstructured, diverse "big data" sources of machine acquire data to generate actionable insights and foresights for real life business problem solutions and product features development and enhancements. The Data Scientist Senior will be a member of a close-knit team of data analysts, data scientists, and software developers analyzing large, multidimensional datasets from internal and external sources. The Data Scientist Senior will develop and apply novel models on customer data in order to help identify areas to increase profits and reduce losses. Essential Duties and Responsibilities: Designs and implementation of Regression based predictive models incorporating diverse data types. Integrates state-of-the-art machine learning algorithms as well as the development of new methods. Partners with experimentalists to translate models into testable hypotheses. Develops tools to support analysis and visualization of large datasets. Develops, codes software programs, implements industry standard auto ML models (Speech, Computer vision, Text Data), Statistical models, relevant ML models (devices/machine acquired data), AI models and algorithms. Identifies meaningful foresights based on predictive ML models from large data and metadata sources; interprets and communicates foresights, insights and findings from experiments to product managers, service managers, business partners and business managers. Makes use of Rapid Development Tools (Business Intelligence Tools, Graphics Libraries, Data modelling tools) to effectively communicate research findings using visual graphics, Data Models, machine learning model features, feature engineering / transformations to relevant stakeholders Analyze, review and track trends and tools in Data Science, Machine Learning, Augmented Intelligence, Artificial Intelligence and IoT space to help in making build/buy/partner recommendations Interacts with Cross-Functional teams to identify questions and issues for data engineering, machine learning models feature engineering. Evaluates and makes recommendations to evolve data collection mechanism for Data capture to improve efficacy of machine learning models prediction. Helps efficiently maintain cloud Infrastructure for research, staging and beta/pilot environment (from costs perspective for working with huge data sets) Mentors, guides, and indirectly leads less experienced Data Scientist peers Drives innovation by fostering open, high energy, collaborative environment; lead participation in innovation summit and expos, recommend relevant training and conference for employees to attend, publish papers and patent disclosures Meets with customers, partners, product managers and business leaders to present findings, predictions, foresights; Gather customer specific requirements of business problems/processes; Identify data collection constraints and alternatives for implementation of models. Minimum Qualifications: Bachelor's in Computer Science, Machine learning, or related discipline. 5+ years experience required. Strong background in statistics, machine learning, deep learning and programming necessary, along with time series forecasting. Hands-on experience with the Azure cloud platform, particularly Azure Databricks. Proficient in large-scale data processing using PySpark and querying with Databricks SQL. Demonstrable experience building and deploying end-to-end ML solutions. Familiarity with CI/CD principles and tools like GitHub Actions, and experience with automated deployment frameworks such as Databricks Asset Bundles. Preferred Qualifications: Master's preferred. Experience in solving large-scale real-world industry problems, preferably in collaboration with cross-functional, multidisciplinary teams. Knowledge of statistical programming techniques and languages (e.g., R, Python, Java, etc.). Working knowledge of common machine learning and deep learning approaches (e.g. regression, clustering, classification, dimensionality reduction, supervised and unsupervised techniques, Bayesian reasoning, boosting, random forests, deep learning) and data analysis packages (e.g. scikit-learn, pyclustering, pathways analysis, MLlib). Prior experience with Tensorflow. Prior experience in Natural Language Processing using NLTK. Retail industry experience desired. Experience using cloud compute (e.g. Google Cloud Platform, AWS, Azure). Familiarity with NoSQL databases, graphical analyses, and large-scale data processing frameworks (e.g. Apache Spark). Solid understanding of data structures, software design and architecture.Ability to work independently and take initiative, but also a cooperative team player. Equal Opportunity Employer: Zebra is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability and protected veteran status, or any other basis prohibited by law. If you are an individual with a disability and need assistance in applying for a position, please contact us at . Know Your Rights: - 10/EEOC_KnowYourRights_screen_reader_10_20.pdf Conozca sus Derechos: -_EEOC_KnowYourRightsSp_10_20.pdf We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation at . Pay Range: $108,960.00 - $163,440.00 Annual Incentive Compensation: In addition to base pay, Zebra offers this role the opportunity to earn a performance-based annual cash incentive, at a target equal to 12% of base pay, in accordance with the terms of the applicable incentive plan. Zebra Total Rewards: Zebra Total Rewards includes more than just pay and is structured to meet the needs of our changing global business and evolving talent. We are committed to providing our employees with a benefits program that is comprehensive and competitive - including healthcare, wellness, inclusion networks, and continued learning and development offerings. We offer community service days, in addition to the traditional insurances, compensation, parental leave, employee assistance program and paid time off offerings depending on the country where you work. Salary offered will vary depending on your location, job-related skills, knowledge, and experience. Additionally, all Zebra roles are eligible for cash incentive programs. For example, sales roles have additional opportunity to earn substantial variable compensation tied to quota achievement. In most other roles, the Zebra annual cash incentive program links Company and individual performance together. Some roles may also be eligible for long-term incentive equity awards. Benefits: We understand the importance of work-life balance and wellbeing, which is why we offer flexibility for our teams including: hybrid work, adaptable hours, Summer Flex Fridays, Focus Fridays, and an annual companywide well-being day to promote revitalization and success. Job Posting Statement: To protect candidates from falling victim to online fraudulent activity involving fake job postings and employment offers, please be aware our recruiters will always connect with you email accounts. Applications are only accepted through our applicant tracking system and only accept personal identifying information through that system. Our Talent Acquisition team will not ask for you to provide personal identifying information via e-mail or outside of the system. If you are a victim of identity theft contact your local police department. AI Technology Statement: Zebra Technologies leverages AI technology to evaluate job applications using objective, job-relevant criteria. This approach enhances efficiency and promotes fairness in the hiring process. However, every decision regarding interviews and hiring is made by our dedicated team . click apply for full job details

Job Description About the role DataAnnotation is looking for an experienced Computational Chemist - AI Trainer to design chemistry problems that today's leading AI models still get wrong. Models routinely propose impossible mechanisms and syntheses that no bench chemist would attempt. You bring the subject knowledge to catch these errors, and the tooling to prove them. This is a new and growing area for our company, focused on teaching AI to reason correctly rather than plausibly. The work combines deep chemical knowledge with strong coding skills: you design, test, and validate complex tasks yourself. Your task and your solution become the ground truth a model is scored against, so accuracy is the entire job. Advantages of contracting with us: You will be able to choose which projects you want to work on and when. You work on your own schedule, on your own computer, from the comfort of your own home. Projects are paid hourly up to $40 - $125 USD per hour, with bonuses on high-quality work. Responsibilities: Design complex chemistry tasks in your area of expertise. Define what a correct answer looks like and the standard a model has to meet. Test and validate the task yourself before you submit it. Write a step-by-step solution another researcher could reproduce. Design tasks a model cannot solve by replaying a known answer, and flag where its chemical reasoning breaks: impossible mechanisms, unsafe conditions, unphysical results, misread spectra. Work across computational and physical chemistry, cheminformatics, molecular modeling, and analytical workflows. Qualifications: Fluency in English (native or bilingual level). An active research background in chemistry or chemical engineering: advanced graduate student, postdoc, academic researcher, or industry practitioner. Strong coding skills. You write Python and work comfortably at a terminal as part of your research, not as a side interest. Familiarity with reproducible or containerized environments such as Docker is a plus. Note: Payment is made via PayPal. We will never ask for any money from you. PayPal will handle any currency conversions from USD. Only applicants in Australia, Canada, Ireland, New Zealand, the United Kingdom, and the United States will be considered for this role. This is an independent contractor position.

Job Description About the role DataAnnotation is looking for an experienced Computational Chemist - AI Trainer to design chemistry problems that today's leading AI models still get wrong. Models routinely propose impossible mechanisms and syntheses that no bench chemist would attempt. You bring the subject knowledge to catch these errors, and the tooling to prove them. This is a new and growing area for our company, focused on teaching AI to reason correctly rather than plausibly. The work combines deep chemical knowledge with strong coding skills: you design, test, and validate complex tasks yourself. Your task and your solution become the ground truth a model is scored against, so accuracy is the entire job. Advantages of contracting with us: You will be able to choose which projects you want to work on and when. You work on your own schedule, on your own computer, from the comfort of your own home. Projects are paid hourly up to $40 - $125 USD per hour, with bonuses on high-quality work. Responsibilities: Design complex chemistry tasks in your area of expertise. Define what a correct answer looks like and the standard a model has to meet. Test and validate the task yourself before you submit it. Write a step-by-step solution another researcher could reproduce. Design tasks a model cannot solve by replaying a known answer, and flag where its chemical reasoning breaks: impossible mechanisms, unsafe conditions, unphysical results, misread spectra. Work across computational and physical chemistry, cheminformatics, molecular modeling, and analytical workflows. Qualifications: Fluency in English (native or bilingual level). An active research background in chemistry or chemical engineering: advanced graduate student, postdoc, academic researcher, or industry practitioner. Strong coding skills. You write Python and work comfortably at a terminal as part of your research, not as a side interest. Familiarity with reproducible or containerized environments such as Docker is a plus. Note: Payment is made via PayPal. We will never ask for any money from you. PayPal will handle any currency conversions from USD. Only applicants in Australia, Canada, Ireland, New Zealand, the United Kingdom, and the United States will be considered for this role. This is an independent contractor position.

Job Description About the role DataAnnotation is looking for an experienced Computational Chemist - AI Trainer to design chemistry problems that today's leading AI models still get wrong. Models routinely propose impossible mechanisms and syntheses that no bench chemist would attempt. You bring the subject knowledge to catch these errors, and the tooling to prove them. This is a new and growing area for our company, focused on teaching AI to reason correctly rather than plausibly. The work combines deep chemical knowledge with strong coding skills: you design, test, and validate complex tasks yourself. Your task and your solution become the ground truth a model is scored against, so accuracy is the entire job. Advantages of contracting with us: You will be able to choose which projects you want to work on and when. You work on your own schedule, on your own computer, from the comfort of your own home. Projects are paid hourly up to $40 - $125 USD per hour, with bonuses on high-quality work. Responsibilities: Design complex chemistry tasks in your area of expertise. Define what a correct answer looks like and the standard a model has to meet. Test and validate the task yourself before you submit it. Write a step-by-step solution another researcher could reproduce. Design tasks a model cannot solve by replaying a known answer, and flag where its chemical reasoning breaks: impossible mechanisms, unsafe conditions, unphysical results, misread spectra. Work across computational and physical chemistry, cheminformatics, molecular modeling, and analytical workflows. Qualifications: Fluency in English (native or bilingual level). An active research background in chemistry or chemical engineering: advanced graduate student, postdoc, academic researcher, or industry practitioner. Strong coding skills. You write Python and work comfortably at a terminal as part of your research, not as a side interest. Familiarity with reproducible or containerized environments such as Docker is a plus. Note: Payment is made via PayPal. We will never ask for any money from you. PayPal will handle any currency conversions from USD. Only applicants in Australia, Canada, Ireland, New Zealand, the United Kingdom, and the United States will be considered for this role. This is an independent contractor position.