7 jobs found

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.

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 Senior Engineer, Machine Learning Compensation: $130,000-$140,000 + 6% Annual Bonus The Senior Engineer, Machine Learning will lead the development, testing, validation, and deployment of machine learning models that improve product performance, installation quality, outage detection, and equipment reliability. This role focuses on analyzing large-scale data generated by millions of deployed meters, sensors, radios, and connected devices to identify performance issues, detect anomalies, and drive actionable solutions. A critical requirement for this position is hands-on experience personally building and training machine learning models, along with implementing anomaly detection algorithms within those models. Candidates must have direct experience developing these models, not simply using existing models or supporting another team's development efforts. Explore the Depth of the Role Machine Learning Model Development Personally build, train, test, and deploy machine learning models, including predictive models, classifiers, and anomaly detection algorithms. Develop time-series data mining and anomaly detection solutions to identify unusual behavior, performance degradation, and potential equipment failures. Design and maintain end-to-end machine learning pipelines, from data preparation and model training through validation, deployment, and production monitoring. Monitor model performance, identify model drift, and implement improvements to maintain accuracy and reliability. Establish testing and validation methods to confirm model results against real-world data. Integrate model outputs into reporting tools and dashboards to support engineering and customer-facing teams. Technical Leadership & Collaboration Lead machine learning projects and investigations, defining technical requirements, scope, schedules, and deliverables. Establish model development standards and validation criteria. Mentor engineers and provide guidance on data preparation, model development, and deployment practices. Collaborate with software developers, data engineers, data scientists, and reliability and quality engineering teams. Communicate technical findings and recommendations to leadership and non-technical stakeholders. Data Infrastructure & Integration Develop and maintain data pipelines integrating internal and external sources, including sensor, carrier network, weather, and manufacturing test data. Work with AWS cloud infrastructure and machine learning platforms, including SageMaker, S3, EC2, ECS, and Glue. Utilize data technologies such as Elasticsearch, Amazon Redshift, and NoSQL databases. Support scalable data architecture and near-real-time analysis of large-scale time-series datasets. Qualifications: Education Bachelor's or Master's degree in Engineering, Mathematics, Statistics, Computer Science, Data Science, Data Analytics, or a related field. Equivalent practical experience may be considered. Required Experience & Technical Skills 5+ years of experience in machine learning, data science, or applied analytics. Demonstrated experience personally building and training machine learning models. Direct experience implementing anomaly detection algorithms within machine learning models you have developed. This is mandatory. Strong proficiency in Python and SQL. Experience with predictive modeling, classification, statistical modeling, and time-series analysis. Experience working with large-scale sensor, IoT, endpoint, or other time-series data. Experience designing, validating, deploying, and monitoring machine learning models in production. Familiarity with GitHub, Jupyter Notebooks, and data visualization tools. Experience with cloud-based machine learning platforms, preferably AWS. Familiarity with Elasticsearch, Amazon Redshift, Grafana, and NoSQL data stores. Preferred Qualifications Experience with IoT devices, meters, sensors, radios, or connectivity equipment. Experience managing machine learning pipelines from development through production monitoring. Understanding of product reliability, device hardware, and failure analysis. Experience integrating third-party data sources into machine learning pipelines. Ability to independently lead technical projects and communicate complex findings. Familiarity with Lean Six Sigma or other continuous improvement methodologies. Competitive Total Rewards Competitive base salary of $130,000-$140,000 plus a 6% annual bonus. Eligible for annual pay increases. Comprehensive health, vision, and dental coverage. 15 days of paid time off plus 11 paid holidays. Retirement savings plan with a 25% employer match on contributions up to 7% of eligible compensation. Additional employer contribution of 5% of eligible compensation to a Defined Contribution account annually. Access to a certified financial planner at no cost. Employer-paid basic life insurance, short-term disability, and Employee Assistance Program. Tuition reimbursement up to $5,250. Voluntary life insurance, long-term disability, accident, and critical illness coverage. Health Savings Account (HSA) and Flexible Spending Account (FSA) options.

About Us Visa is a world leader in payments technology, facilitating transactions between consumers, merchants, financial institutions and government entities across more than 200 countries and territories, dedicated to uplifting everyone, everywhere by being the best way to pay and be paid. At Visa, you'll have the opportunity to create impact at scale - tackling meaningful challenges, growing your skills and seeing your contributions impact lives around the world. Join Visa and do work that matters - to you, to your community, and to the world. Progress starts with you. Job Description Overview: Visa Acceptance Intelligence is the foundation for innovative payment experiences through secure, resilient services and a broad ecosystem of technology and payments partners. We are building on that foundation to help businesses make smarter decisions with data. At Visa, we believe AI can transform how commerce works. Through Acceptance Analytics and Intelligence and Visa Intelligent Commerce, we're developing data products that connect information and surface actionable insights for Visa clients. We are looking for a curious, versatile Senior Applied Scientist to help advance this work. You'll partner with scientists, engineers, product leaders, and business stakeholders to design, build, and evaluate intelligent products-from forecasting and NLP solutions to carefully evaluate applications of large language models (LLMs). You will help turn promising ideas into reliable, useful capabilities for customers. If you enjoy working across research and production, questioning assumptions, and solving meaningful problems at scale, we'd like to meet you. Essential Functions: Develop and evaluate time-series analysis and forecasting approaches for business and payments use cases. Apply NLP and LLM techniques to data and intelligence products, including fine-tuning or other model-adaptation methods when appropriate. Design rigorous evaluation strategies for LLM-powered systems, including task-specific quality measures and assessment of reliability. Explore how LLMs can complement established analytical methods in time-series workflows, selecting techniques based on the problem, data, and evaluation results. Improve data extraction and preparation through data wrangling, complex data modeling, and AI-assisted approaches; raise the quality, completeness, and usability of data. Follow and strengthen data-management and governance practices that support high-quality, dependable products. Translate sophisticated technical and business needs into clear specifications with product owners, business partners, cybersecurity teams, and other collaborators. Identify dependencies across upstream and downstream systems and help shape solutions that work across the platform. Find meaningful patterns across relevant data sources and turn them into insights, product opportunities, and recommendations for future platform improvements. Contribute to technical roadmaps and, where relevant, recommend software upgrades or security remediation. Visa requires at least 3 days in office, expectations of these days will be confirmed by your Hiring Manager. Qualifications Basic Qualifications: 8+ years of relevant work experience with a Bachelor's Degree or at least 5 years of experience with an Advanced Degree (e.g., Master's, MBA, JD, MD) or 2 years of work experience with a PhD, OR 11+ years of relevant work experience. Preferred Qualifications: 9 or more years of relevant work experience with a Bachelor's Degree or 7 or more relevant years of experience with an Advanced Degree (e.g., Master's, MBA, JD, MD) or 3 or more years of experience with a PhD. PhD degree in computer science or relevant area. Experience in solving data problems using data technologies (e.g., Hadoop, Hive, Kafka, Redis, NoSQL, RDBMS). Experience with Big Data and Analytics in general leveraging technologies like Hadoop, Spark, Flink and MapReduce. Information for US Applicants For roles located in the US, the estimated salary range for this position is $162,500.00 to $ 260,400.00 USD per year, which may include potential sales incentive payments (if applicable). Salary may vary depending on job-related factors which may include knowledge, skills, experience, and location. In addition, this position may be eligible for bonus and equity.Visa has a comprehensive benefits package for which this position may be eligible that includes Medical, Dental, Vision, 401(k), FSA/HSA, Life Insurance, Paid Time Off, and Wellness Program. Work Hours Varies upon the needs of the department. Travel Requirements This position requires travel 5-10% of the time. Mental/Physical Requirements This position will be performed in an office setting. The position will require the incumbent to sit and stand at a desk, communicate in person and by telephone, frequently operate standard office equipment, such as telephones and computers. Visa is an EEO Employer Qualified applicants will receive consideration for employment without regard to race, color religion, sex, national origin, sexual orientation, gender identity, disability or protect veteran status. Visa will also consider for employment qualified applicants with criminal histories in a manner consistent with the EEOC guidelines and applicable local law.

Starbucks is seeking a Senior/Lead Decision Scientist to shape data-driven strategy across its global coffeehouse business. In this role, you will partner with Retail, Supply Chain, and Marketing teams to design experiments, build predictive and optimization models, and translate complex data into clear, actionable insights. You will lead advanced analytics projects, define key metrics, and develop dashboards that guide pricing, assortment, and customer experience decisions. You will mentor other analysts, champion best practices in experimentation and data ethics, and help embed a culture of evidence-based decision making across Starbucks. Responsibilities Lead design and analysis of experiments to optimize pricing, promotions, and customer experience Build and deploy predictive and optimization models to support retail and supply chain decisions Develop dashboards and self-serve analytics tools for stakeholders Define and refine key performance metrics and measurement frameworks Translate complex analytical findings into clear, actionable business recommendations Partner with cross-functional teams in Marketing, Operations, and Supply Chain Mentor and guide junior analysts and decision scientists Promote best practices in data quality, experimentation, and analytics governance Required Skills Statistical modeling A/B testing and experimentation design Machine learning SQLPython or RData visualization (Tableau/Power BI) Predictive analytics Optimization and forecasting Big data tools (e.g., Spark, Hive) Business intelligence and dashboarding

Starbucks is seeking a Senior/Lead Decision Scientist to drive data-informed strategy across our global coffeehouse business. In this role, you'll translate complex data into clear insights that improve customer experience, store operations, and digital engagement. You'll design experiments, develop predictive and prescriptive models, and build dashboards that guide leaders in merchandising, pricing, and loyalty initiatives. Partnering with cross-functional teams, you'll define metrics, validate hypotheses, and tell compelling data stories. You'll mentor junior analysts, champion data quality and governance, and help shape a culture of evidence-based, ethical decision-making at scale. Responsibilities Lead design and execution of experiments and A/B tests to optimize customer experience, pricing, and promotions. Develop predictive and prescriptive models to support demand forecasting, personalization, and store operations. Translate complex data into clear, actionable insights and narratives for business and executive stakeholders. Build and maintain dashboards, reports, and self-service analytics tools for cross-functional teams. Define and refine key performance metrics for digital, retail, and loyalty initiatives. Collaborate with product, marketing, operations, and finance to identify and prioritize analytics opportunities. Ensure data quality, governance, and proper use of data across projects. Mentor and guide junior analysts and data scientists on best practices and frameworks. Evaluate and implement new analytical methods, tools, and data sources to enhance decision-making. Champion a culture of ethical, evidence-based decision-making aligned with Starbucks values. Required Skills Data science Statistical analysis A/B testing and experimentation Predictive modeling Machine learning SQLPython or RData visualization (Tableau/Power BI) Data storytelling and communication Business intelligence and reporting

Starbucks is seeking a Senior/Lead Decision Scientist to drive data-informed strategy across our global coffee and food operations. In this role, you will use advanced analytics, experimentation, and predictive modeling to optimize pricing, product mix, and store performance. You'll partner with operations, marketing, and digital teams to translate complex data into clear insights, dashboards, and recommendations that enhance guest experience and profitability. You will mentor junior analysts, promote data literacy, and help shape a culture of ethical, responsible data use aligned with Starbucks' community and sustainability values. Responsibilities Develop and deploy advanced analytics, forecasting, and predictive models to inform pricing, product, and operational decisions. Design and analyze A/B tests and experiments to evaluate initiatives across stores and digital channels. Build clear dashboards and self-service BI tools for stakeholders in operations, marketing, and digital. Translate complex data into actionable insights and strategic recommendations for senior leaders. Partner with cross-functional teams to define metrics, KPIs, and measurement frameworks. Mentor and guide junior analysts and data scientists, promoting best practices in analytics and coding. Ensure data quality, governance, and ethical use of customer and operational data. Communicate findings through compelling presentations, visualizations, and written summaries. Collaborate with technology teams to improve data pipelines, tooling, and analytics platforms. Support long-term analytics roadmap aligned with Starbucks' sustainability, community, and growth goals. Required Skills SQLPython or RStatistical modeling Predictive analytics A/B testing and experimentation Machine learning fundamentals Data visualization (e.g., Tableau, Power BI) Big data tools (e.g., Spark, Hive) Dashboarding and reporting Business intelligence in retail/hospitality Data storytelling Forecasting and time-series analysis