US Navy
Durango, Colorado
Job Title: Advanced Electronics / Computer Field (ET/FC) Category / Component: Enlisted • Active Overview The Advanced Electronics and Computer Field trains Sailors to maintain, operate, and repair some of the Navy's most sophisticated electronics and computing systems, including radar, communications, navigation, local area networks, weapons fire control, and Aegis combat systems. ETs and FCs form the backbone of a ship's Combat Systems department aboard carriers, cruisers, destroyers, and other surface combatants, as well as at repair and technical activities ashore. Key Responsibilities Serve as an Electronics Technician (ET) or Fire Controlman (FC) after training, based on performance and Navy needs; as an ET, maintain and repair radar, communication, and navigation equipment including transmitters, receivers, displays, and shipboard communications suites such as SATCOM and HF; as an FC, operate, maintain, and repair fire control radars, computers, large screen displays, local area networks, weapon control consoles, and automatic gun systems; troubleshoot complex electronic and electro mechanical faults using technical documentation, test equipment, and established procedures; maintain configuration control, documentation, and logs that support inspections, certifications, and combat system readiness. What to Expect Hands on technical work that blends classroom, computer based training, and intensive lab practice; frequent troubleshooting under time pressure to restore mission critical combat systems and communications; strict adherence to safety procedures, configuration control, technical documentation, and test routines; team based maintenance and watchstanding afloat and ashore, often on rotating shifts to support around the clock operations; progressive responsibility as you qualify on systems, earn Navy Enlisted Classifications, and advance in rate. Work Environment Assignments aboard surface combatants such as aircraft carriers, Aegis cruisers and destroyers, and amphibious ships, as well as at shore based repair and technical facilities; daily work in combat systems spaces, radar rooms, communications centers, electronics shops, and shipboard network spaces; a mix of lab style environments and shipboard spaces with noise, ladders, confined areas, and occasional exposure to heat or weather when working on topside equipment. Pathways, Training & Advancement Recruit Training followed by Apprentice Technical Training at Great Lakes, Illinois, covering basic electronics, circuitry, safety, digital theory, microcomputers, fiber optics, test equipment, and troubleshooting; strand training in either the Fire Controlman or Electronics Technician track, with FCs focusing on radar, ballistics, and fire control basics, and ETs focusing on communications suites and radar systems; follow on A School and platform or system specific C Schools, often with college credit recommended by the American Council on Education; accelerated advancement to E4 upon completion of initial school training and all advancement requirements, with continued promotion based on performance, time in rate, and professional development. Enlist under the Advanced Electronics and Computer Field program, with final placement into the Electronics Technician or Fire Controlman rating during initial training at Great Lakes, based on performance and Navy needs; maintain AECF eligibility throughout training in order to retain any accelerated advancement benefits; fleet conversion into ET or FC from another rating may be possible for qualified Sailors, subject to screening and community manning. Qualifications All Navy jobs require meeting general enlistment or commissioning standards, which typically include: Eligibility to serve in the United States Navy, which may involve United States citizenship or other legal residency and work status, depending on the program and current law and policy A high school diploma or equivalent for enlisted positions, and a bachelor's or qualifying professional degree for officer positions Meeting age limits that vary by program and are set in law and Navy policy. Some communities have more restrictive age ranges Meeting medical, vision, and dental standards, including body composition and physical fitness requirements, with some jobs requiring more demanding standards Meeting character and conduct standards, including background screening Achieving required test scores for your program, such as the Armed Services Vocational Aptitude Battery for enlisted roles or officer qualification tests for officer programs Eligibility for a security clearance when required for your rating or designator Additional qualifications can include specific skills, education, licensure, or experience that are unique to a job or community and will be reviewed with you by a recruiter. Additional qualifications for this job may include: Normal hearing and color perception to work safely with electronic displays and color coded wiring and schematics; strong arithmetic and computing aptitude, with the ability to learn digital theory and complex technical systems; physical strength and manual dexterity to handle equipment, tools, ladders, and shipboard environments. Education Education benefits are available through standard Navy programs such as Tuition Assistance, the Post-9/11 GI Bill, ACE-recommended college credit for Navy training, Navy COOL-funded certifications, USMAP apprenticeships, and other Navy College Program opportunities. Specific options depend on the Sailor's status, training, and current Navy policy. Pay, Benefits & Service Pay, benefits, and service commitments follow standard Navy Active and/or Reserve policies for this type of role, including basic pay, allowances when eligible, health coverage, and retirement options. Exact entitlements, special pays, and service obligations depend on program, component, years of service, and current law and Navy guidance. Incentives Incentives such as bonuses, special pays, and loan repayment may be available at times for specific ratings or communities, but they change frequently and cannot be guaranteed. Applicants must confirm current incentives and eligibility with an official Navy recruiter or authoritative Navy source. Notes and Disclaimers This description is a general overview of typical duties, training, and opportunities in this community. It does not replace official Navy instructions, policies, or contracts and does not guarantee specific assignments, training, incentives, or outcomes. Actual opportunities depend on Navy needs, individual performance, screening results, and current law and policy.
Job Title: Advanced Electronics / Computer Field (ET/FC) Category / Component: Enlisted • Active Overview The Advanced Electronics and Computer Field trains Sailors to maintain, operate, and repair some of the Navy's most sophisticated electronics and computing systems, including radar, communications, navigation, local area networks, weapons fire control, and Aegis combat systems. ETs and FCs form the backbone of a ship's Combat Systems department aboard carriers, cruisers, destroyers, and other surface combatants, as well as at repair and technical activities ashore. Key Responsibilities Serve as an Electronics Technician (ET) or Fire Controlman (FC) after training, based on performance and Navy needs; as an ET, maintain and repair radar, communication, and navigation equipment including transmitters, receivers, displays, and shipboard communications suites such as SATCOM and HF; as an FC, operate, maintain, and repair fire control radars, computers, large screen displays, local area networks, weapon control consoles, and automatic gun systems; troubleshoot complex electronic and electro mechanical faults using technical documentation, test equipment, and established procedures; maintain configuration control, documentation, and logs that support inspections, certifications, and combat system readiness. What to Expect Hands on technical work that blends classroom, computer based training, and intensive lab practice; frequent troubleshooting under time pressure to restore mission critical combat systems and communications; strict adherence to safety procedures, configuration control, technical documentation, and test routines; team based maintenance and watchstanding afloat and ashore, often on rotating shifts to support around the clock operations; progressive responsibility as you qualify on systems, earn Navy Enlisted Classifications, and advance in rate. Work Environment Assignments aboard surface combatants such as aircraft carriers, Aegis cruisers and destroyers, and amphibious ships, as well as at shore based repair and technical facilities; daily work in combat systems spaces, radar rooms, communications centers, electronics shops, and shipboard network spaces; a mix of lab style environments and shipboard spaces with noise, ladders, confined areas, and occasional exposure to heat or weather when working on topside equipment. Pathways, Training & Advancement Recruit Training followed by Apprentice Technical Training at Great Lakes, Illinois, covering basic electronics, circuitry, safety, digital theory, microcomputers, fiber optics, test equipment, and troubleshooting; strand training in either the Fire Controlman or Electronics Technician track, with FCs focusing on radar, ballistics, and fire control basics, and ETs focusing on communications suites and radar systems; follow on A School and platform or system specific C Schools, often with college credit recommended by the American Council on Education; accelerated advancement to E4 upon completion of initial school training and all advancement requirements, with continued promotion based on performance, time in rate, and professional development. Enlist under the Advanced Electronics and Computer Field program, with final placement into the Electronics Technician or Fire Controlman rating during initial training at Great Lakes, based on performance and Navy needs; maintain AECF eligibility throughout training in order to retain any accelerated advancement benefits; fleet conversion into ET or FC from another rating may be possible for qualified Sailors, subject to screening and community manning. Qualifications All Navy jobs require meeting general enlistment or commissioning standards, which typically include: Eligibility to serve in the United States Navy, which may involve United States citizenship or other legal residency and work status, depending on the program and current law and policy A high school diploma or equivalent for enlisted positions, and a bachelor's or qualifying professional degree for officer positions Meeting age limits that vary by program and are set in law and Navy policy. Some communities have more restrictive age ranges Meeting medical, vision, and dental standards, including body composition and physical fitness requirements, with some jobs requiring more demanding standards Meeting character and conduct standards, including background screening Achieving required test scores for your program, such as the Armed Services Vocational Aptitude Battery for enlisted roles or officer qualification tests for officer programs Eligibility for a security clearance when required for your rating or designator Additional qualifications can include specific skills, education, licensure, or experience that are unique to a job or community and will be reviewed with you by a recruiter. Additional qualifications for this job may include: Normal hearing and color perception to work safely with electronic displays and color coded wiring and schematics; strong arithmetic and computing aptitude, with the ability to learn digital theory and complex technical systems; physical strength and manual dexterity to handle equipment, tools, ladders, and shipboard environments. Education Education benefits are available through standard Navy programs such as Tuition Assistance, the Post-9/11 GI Bill, ACE-recommended college credit for Navy training, Navy COOL-funded certifications, USMAP apprenticeships, and other Navy College Program opportunities. Specific options depend on the Sailor's status, training, and current Navy policy. Pay, Benefits & Service Pay, benefits, and service commitments follow standard Navy Active and/or Reserve policies for this type of role, including basic pay, allowances when eligible, health coverage, and retirement options. Exact entitlements, special pays, and service obligations depend on program, component, years of service, and current law and Navy guidance. Incentives Incentives such as bonuses, special pays, and loan repayment may be available at times for specific ratings or communities, but they change frequently and cannot be guaranteed. Applicants must confirm current incentives and eligibility with an official Navy recruiter or authoritative Navy source. Notes and Disclaimers This description is a general overview of typical duties, training, and opportunities in this community. It does not replace official Navy instructions, policies, or contracts and does not guarantee specific assignments, training, incentives, or outcomes. Actual opportunities depend on Navy needs, individual performance, screening results, and current law and policy.
Harnham
Anaheim, California
Clinical Data Scientist - Hybrid or Remote Overview A growing organization in the healthcare and life sciences technology space is seeking a Clinical Data Scientist to support the final stages of its clinical data pipeline. This role focuses on transforming complex clinical datasets into clean, standardized, analysis ready deliverables for use in clinical research and regulatory submissions. The ideal candidate is detail oriented, technically versatile, and passionate about delivering high quality clinical data. You will work closely with cross functional teams-including Clinical Operations, Data Platform Engineering, and AI/ML teams-to ensure accuracy, traceability, and compliance across all data outputs. This role may be hybrid or fully remote; candidates in the SF Bay Area are preferred but not required. Key Responsibilities Data Transformation & Delivery Convert raw, manually abstracted, and AI processed clinical data into standardized formats (e.g., CDISC SDTM/ADaM) or client specific data models. Build analysis ready datasets from diverse sources including EMRs, EDC systems, and internal abstraction tools. Statistical Programming & Reporting Develop and generate Tables, Listings, and Figures (TLFs) for clinical study reports and interim analyses using SAS, R, or Python. Data Integrity & Quality Control Perform comprehensive data cleaning and validation checks. Investigate discrepancies to distinguish between true clinical complexity and upstream data errors. Cross Team Collaboration Partner with engineering teams to automate data cleaning and validation workflows. Serve as an early tester and internal customer for new data infrastructure tools. Documentation Create and maintain clear documentation including data specifications, derivation logic, reviewers' guides, and Define.xml to support audits and regulatory submissions. Ad Hoc & Exploratory Analysis Support internal and external stakeholders with data queries, one off analyses, and insights that demonstrate the value of the platform. Ethical & Compliant Data Management Follow all applicable privacy, security, and compliance requirements (e.g., HIPAA). Promote ethical handling of sensitive clinical data. Qualifications Education BSc or MSc in Statistics, Mathematics, Computer Science, Life Sciences, or a related field. Experience 2-5+ years in clinical data science, statistical programming, or clinical data management within Pharma, Biotech, or related environment. Technical Skills Strong proficiency in SAS, R, Python, and SQL. Familiarity with version control (Git) preferred. Experience with CDISC SDTM/ADaM standards. Understanding of oncology endpoints (e.g., RECIST, survival analysis) and real world data (RWD) is a plus. Core Competencies Demonstrated expertise in cleaning and stitching complex datasets. Ability to work with unstructured text or NLP derived outputs is highly desirable. Exceptional attention to detail and accuracy. Strong written and verbal communication skills. Self directed, curious, humble, and collaborative. Additional Information Travel: Up to 5% Compensation: Competitive salary range based on experience, qualifications, and location. Eligible for equity, annual performance bonus, and comprehensive benefits.
Clinical Data Scientist - Hybrid or Remote Overview A growing organization in the healthcare and life sciences technology space is seeking a Clinical Data Scientist to support the final stages of its clinical data pipeline. This role focuses on transforming complex clinical datasets into clean, standardized, analysis ready deliverables for use in clinical research and regulatory submissions. The ideal candidate is detail oriented, technically versatile, and passionate about delivering high quality clinical data. You will work closely with cross functional teams-including Clinical Operations, Data Platform Engineering, and AI/ML teams-to ensure accuracy, traceability, and compliance across all data outputs. This role may be hybrid or fully remote; candidates in the SF Bay Area are preferred but not required. Key Responsibilities Data Transformation & Delivery Convert raw, manually abstracted, and AI processed clinical data into standardized formats (e.g., CDISC SDTM/ADaM) or client specific data models. Build analysis ready datasets from diverse sources including EMRs, EDC systems, and internal abstraction tools. Statistical Programming & Reporting Develop and generate Tables, Listings, and Figures (TLFs) for clinical study reports and interim analyses using SAS, R, or Python. Data Integrity & Quality Control Perform comprehensive data cleaning and validation checks. Investigate discrepancies to distinguish between true clinical complexity and upstream data errors. Cross Team Collaboration Partner with engineering teams to automate data cleaning and validation workflows. Serve as an early tester and internal customer for new data infrastructure tools. Documentation Create and maintain clear documentation including data specifications, derivation logic, reviewers' guides, and Define.xml to support audits and regulatory submissions. Ad Hoc & Exploratory Analysis Support internal and external stakeholders with data queries, one off analyses, and insights that demonstrate the value of the platform. Ethical & Compliant Data Management Follow all applicable privacy, security, and compliance requirements (e.g., HIPAA). Promote ethical handling of sensitive clinical data. Qualifications Education BSc or MSc in Statistics, Mathematics, Computer Science, Life Sciences, or a related field. Experience 2-5+ years in clinical data science, statistical programming, or clinical data management within Pharma, Biotech, or related environment. Technical Skills Strong proficiency in SAS, R, Python, and SQL. Familiarity with version control (Git) preferred. Experience with CDISC SDTM/ADaM standards. Understanding of oncology endpoints (e.g., RECIST, survival analysis) and real world data (RWD) is a plus. Core Competencies Demonstrated expertise in cleaning and stitching complex datasets. Ability to work with unstructured text or NLP derived outputs is highly desirable. Exceptional attention to detail and accuracy. Strong written and verbal communication skills. Self directed, curious, humble, and collaborative. Additional Information Travel: Up to 5% Compensation: Competitive salary range based on experience, qualifications, and location. Eligible for equity, annual performance bonus, and comprehensive benefits.
Harnham
San Francisco, California
AI Engineer 1) Organization Overview (Concise & Neutral) A fast growing oncology focused organization is reinventing how clinical trials operate by integrating them tightly with real world clinical practice. Cross disciplinary teams across healthcare, engineering, AI, and regulatory domains work in a Human in the Loop (HITL) model to deliver regulatory grade outcomes that expand trial access and accelerate cancer drug development. What's Different Clinical trials are embedded within clinical practice-not run in parallel. Hybrid model blending expert abstraction, AI/NLP/LLMs, and EMR integrations. Strong commitment to rigorous testing, validation, privacy, and ethical AI. Mission driven culture with high collaboration and urgency. Why Join Now AI is essential to scaling the business-impact is immediate and visible. Opportunity to build an end to end applied AI stack powering clinical teams. Work across LLMs, CV, and multimodal use cases with strong platform engineering partners. 2) Role Overview - AI Engineer Purpose Design and deliver applied AI systems (LLMs/CV/multimodal) that automate clinical variable extraction and clinical note generation. Work includes repeatable validation, robust documentation, and HITL feedback loops. Strong emphasis on data engineering and backend rigor to ensure model usability and efficiency. Focus Areas LLM development and LLM Ops for text extraction and structuring; some CV/multimodal. Rapid prototyping with high software engineering hygiene. Statistical validation plans, experiment design, and metrics ownership. HITL workflow development to reduce manual QA and improve throughput. Collaboration with Platform Engineering for production alignment. Comprehensive documentation: datasets, experiments, model cards, QA audits. HIPAA aligned safeguards and compliant AI practices. Core Responsibilities Build AI models and pipelines across EMR/EHR, imaging, and clinical documents. Translate ambiguous clinical requirements into measurable ML objectives. Define metrics, design experiments, and estimate/model error. Lead interim QA audit processes and evolve toward AI assisted QA. Partner with data/platform engineers on scalability, data flow, and observability. Champion code quality, experiment tracking, reproducibility, and knowledge capture. Expected Impact (6-12 Months) Deliver validated AI components for abstraction and note generation. Meaningfully reduce manual QA workload through HITL optimization. Standardize testing and documentation frameworks. Establish efficient PySpark/SQL/Postgres data manipulation patterns that accelerate iteration. 3) Product & AI Context In Flight Work LLM powered abstraction and clinical note generation with HITL. Auditing and validation pipelines. Mandate Drive productivity gains for internal labeling/abstraction teams. Build the full applied AI stack: model development, context engineering, QA automation, interfaces. Productionization Deployment owned by Platform Engineering, but AI Engineers write scalable, integration ready code. Data Gravity Data engineering is a major part of the role: PySpark, SQL/Postgres, query optimization, cloud data tooling. 4) Ideal Candidate Profile Background MSc/PhD in CS, EE, Applied Math, Stats, Physics, or equivalent depth via experience. 2-5+ years in AI/ML engineering or applied data science. Healthcare or clinical workflows experience strongly preferred; oncology a plus. Technical Must Haves Expert level Python + strong software engineering practices. Deep learning experience with PyTorch or TensorFlow (LLMs and/or CV). Data engineering: PySpark, SQL, Postgres, data modeling, query tuning. Cloud data platforms (Databricks, S3/Snowflake/Azure/GCP). Experiment design, statistical validation, and error analysis. HITL lifecycle design and feedback integration. Nice to Have Additional languages: R, Java, C++. MLOps fundamentals (versioning, lineage, CI/CD). Prior oncology or clinical trials exposure.
AI Engineer 1) Organization Overview (Concise & Neutral) A fast growing oncology focused organization is reinventing how clinical trials operate by integrating them tightly with real world clinical practice. Cross disciplinary teams across healthcare, engineering, AI, and regulatory domains work in a Human in the Loop (HITL) model to deliver regulatory grade outcomes that expand trial access and accelerate cancer drug development. What's Different Clinical trials are embedded within clinical practice-not run in parallel. Hybrid model blending expert abstraction, AI/NLP/LLMs, and EMR integrations. Strong commitment to rigorous testing, validation, privacy, and ethical AI. Mission driven culture with high collaboration and urgency. Why Join Now AI is essential to scaling the business-impact is immediate and visible. Opportunity to build an end to end applied AI stack powering clinical teams. Work across LLMs, CV, and multimodal use cases with strong platform engineering partners. 2) Role Overview - AI Engineer Purpose Design and deliver applied AI systems (LLMs/CV/multimodal) that automate clinical variable extraction and clinical note generation. Work includes repeatable validation, robust documentation, and HITL feedback loops. Strong emphasis on data engineering and backend rigor to ensure model usability and efficiency. Focus Areas LLM development and LLM Ops for text extraction and structuring; some CV/multimodal. Rapid prototyping with high software engineering hygiene. Statistical validation plans, experiment design, and metrics ownership. HITL workflow development to reduce manual QA and improve throughput. Collaboration with Platform Engineering for production alignment. Comprehensive documentation: datasets, experiments, model cards, QA audits. HIPAA aligned safeguards and compliant AI practices. Core Responsibilities Build AI models and pipelines across EMR/EHR, imaging, and clinical documents. Translate ambiguous clinical requirements into measurable ML objectives. Define metrics, design experiments, and estimate/model error. Lead interim QA audit processes and evolve toward AI assisted QA. Partner with data/platform engineers on scalability, data flow, and observability. Champion code quality, experiment tracking, reproducibility, and knowledge capture. Expected Impact (6-12 Months) Deliver validated AI components for abstraction and note generation. Meaningfully reduce manual QA workload through HITL optimization. Standardize testing and documentation frameworks. Establish efficient PySpark/SQL/Postgres data manipulation patterns that accelerate iteration. 3) Product & AI Context In Flight Work LLM powered abstraction and clinical note generation with HITL. Auditing and validation pipelines. Mandate Drive productivity gains for internal labeling/abstraction teams. Build the full applied AI stack: model development, context engineering, QA automation, interfaces. Productionization Deployment owned by Platform Engineering, but AI Engineers write scalable, integration ready code. Data Gravity Data engineering is a major part of the role: PySpark, SQL/Postgres, query optimization, cloud data tooling. 4) Ideal Candidate Profile Background MSc/PhD in CS, EE, Applied Math, Stats, Physics, or equivalent depth via experience. 2-5+ years in AI/ML engineering or applied data science. Healthcare or clinical workflows experience strongly preferred; oncology a plus. Technical Must Haves Expert level Python + strong software engineering practices. Deep learning experience with PyTorch or TensorFlow (LLMs and/or CV). Data engineering: PySpark, SQL, Postgres, data modeling, query tuning. Cloud data platforms (Databricks, S3/Snowflake/Azure/GCP). Experiment design, statistical validation, and error analysis. HITL lifecycle design and feedback integration. Nice to Have Additional languages: R, Java, C++. MLOps fundamentals (versioning, lineage, CI/CD). Prior oncology or clinical trials exposure.
Harnham
Long Beach, California
Clinical Data Scientist - Hybrid or Remote Overview A growing organization in the healthcare and life sciences technology space is seeking a Clinical Data Scientist to support the final stages of its clinical data pipeline. This role focuses on transforming complex clinical datasets into clean, standardized, analysis ready deliverables for use in clinical research and regulatory submissions. The ideal candidate is detail oriented, technically versatile, and passionate about delivering high quality clinical data. You will work closely with cross functional teams-including Clinical Operations, Data Platform Engineering, and AI/ML teams-to ensure accuracy, traceability, and compliance across all data outputs. This role may be hybrid or fully remote; candidates in the SF Bay Area are preferred but not required. Key Responsibilities Data Transformation & Delivery Convert raw, manually abstracted, and AI processed clinical data into standardized formats (e.g., CDISC SDTM/ADaM) or client specific data models. Build analysis ready datasets from diverse sources including EMRs, EDC systems, and internal abstraction tools. Statistical Programming & Reporting Develop and generate Tables, Listings, and Figures (TLFs) for clinical study reports and interim analyses using SAS, R, or Python. Data Integrity & Quality Control Perform comprehensive data cleaning and validation checks. Investigate discrepancies to distinguish between true clinical complexity and upstream data errors. Cross Team Collaboration Partner with engineering teams to automate data cleaning and validation workflows. Serve as an early tester and internal customer for new data infrastructure tools. Documentation Create and maintain clear documentation including data specifications, derivation logic, reviewers' guides, and Define.xml to support audits and regulatory submissions. Ad Hoc & Exploratory Analysis Support internal and external stakeholders with data queries, one off analyses, and insights that demonstrate the value of the platform. Ethical & Compliant Data Management Follow all applicable privacy, security, and compliance requirements (e.g., HIPAA). Promote ethical handling of sensitive clinical data. Qualifications Education BSc or MSc in Statistics, Mathematics, Computer Science, Life Sciences, or a related field. Experience 2-5+ years in clinical data science, statistical programming, or clinical data management within Pharma, Biotech, or related environment. Technical Skills Strong proficiency in SAS, R, Python, and SQL. Familiarity with version control (Git) preferred. Experience with CDISC SDTM/ADaM standards. Understanding of oncology endpoints (e.g., RECIST, survival analysis) and real world data (RWD) is a plus. Core Competencies Demonstrated expertise in cleaning and stitching complex datasets. Ability to work with unstructured text or NLP derived outputs is highly desirable. Exceptional attention to detail and accuracy. Strong written and verbal communication skills. Self directed, curious, humble, and collaborative. Additional Information Travel: Up to 5% Compensation: Competitive salary range based on experience, qualifications, and location. Eligible for equity, annual performance bonus, and comprehensive benefits.
Clinical Data Scientist - Hybrid or Remote Overview A growing organization in the healthcare and life sciences technology space is seeking a Clinical Data Scientist to support the final stages of its clinical data pipeline. This role focuses on transforming complex clinical datasets into clean, standardized, analysis ready deliverables for use in clinical research and regulatory submissions. The ideal candidate is detail oriented, technically versatile, and passionate about delivering high quality clinical data. You will work closely with cross functional teams-including Clinical Operations, Data Platform Engineering, and AI/ML teams-to ensure accuracy, traceability, and compliance across all data outputs. This role may be hybrid or fully remote; candidates in the SF Bay Area are preferred but not required. Key Responsibilities Data Transformation & Delivery Convert raw, manually abstracted, and AI processed clinical data into standardized formats (e.g., CDISC SDTM/ADaM) or client specific data models. Build analysis ready datasets from diverse sources including EMRs, EDC systems, and internal abstraction tools. Statistical Programming & Reporting Develop and generate Tables, Listings, and Figures (TLFs) for clinical study reports and interim analyses using SAS, R, or Python. Data Integrity & Quality Control Perform comprehensive data cleaning and validation checks. Investigate discrepancies to distinguish between true clinical complexity and upstream data errors. Cross Team Collaboration Partner with engineering teams to automate data cleaning and validation workflows. Serve as an early tester and internal customer for new data infrastructure tools. Documentation Create and maintain clear documentation including data specifications, derivation logic, reviewers' guides, and Define.xml to support audits and regulatory submissions. Ad Hoc & Exploratory Analysis Support internal and external stakeholders with data queries, one off analyses, and insights that demonstrate the value of the platform. Ethical & Compliant Data Management Follow all applicable privacy, security, and compliance requirements (e.g., HIPAA). Promote ethical handling of sensitive clinical data. Qualifications Education BSc or MSc in Statistics, Mathematics, Computer Science, Life Sciences, or a related field. Experience 2-5+ years in clinical data science, statistical programming, or clinical data management within Pharma, Biotech, or related environment. Technical Skills Strong proficiency in SAS, R, Python, and SQL. Familiarity with version control (Git) preferred. Experience with CDISC SDTM/ADaM standards. Understanding of oncology endpoints (e.g., RECIST, survival analysis) and real world data (RWD) is a plus. Core Competencies Demonstrated expertise in cleaning and stitching complex datasets. Ability to work with unstructured text or NLP derived outputs is highly desirable. Exceptional attention to detail and accuracy. Strong written and verbal communication skills. Self directed, curious, humble, and collaborative. Additional Information Travel: Up to 5% Compensation: Competitive salary range based on experience, qualifications, and location. Eligible for equity, annual performance bonus, and comprehensive benefits.
