Clinical Data Scientist Jobs
Clinical Data Scientist jobs are open across pharma, biotech, medical devices, and health systems, from entry-level analyst to principal and director, with specializations in clinical trials, real-world evidence, and regulatory submissions. Scan the live roles below and apply to whichever ones fit.
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Lead Data Scientist – Causal Inference & Clinical Outcomes (Contract)
Location: Remote – United States
Employment Type: Part-Time Contract (1099 Independent Contractor or W-2)
Schedule: 10–15 hours per week initially, with the opportunity to increase hours based on project needs
Compensation: $150 per hour
Reports To: CFO
Engagement Duration: Project-based, with potential for extension or expanded responsibilities
About Jaan Health / Phamily
Jaan Health is an innovative, AI-powered healthcare technology company focused on transforming proactive care management and improving patient outcomes.
Through its proprietary Phamily platform, Jaan Health enables healthcare providers, medical groups, health systems, and Accountable Care Organizations (ACOs) to deliver scalable, high-quality care management services.
Phamily leverages advanced technology and AI-enabled workflows to help providers manage chronic conditions, improve patient engagement, reduce avoidable healthcare utilization, and deliver measurable clinical and financial outcomes.
Learn more at phamily.com.
Position Overview
Jaan Health is seeking a highly experienced Lead Data Scientist specializing in Causal Inference and Clinical Outcomes Research for a flexible, remote consulting engagement.
This opportunity is ideal for a senior-level data scientist, biostatistician, health economist, or Healthcare Economics and Outcomes Research (HEOR) professional who has extensive experience evaluating the impact of healthcare interventions using real-world clinical data.
The consultant will lead the design and execution of a rigorous clinical outcomes study evaluating the effectiveness of Jaan Health's Advanced Primary Care Management (APCM) and Chronic Care Management (CCM) programs.
The primary objective is to establish statistically defensible evidence of program effectiveness, with particular emphasis on:
Hospitalization and Readmission Rates: Determining whether patients enrolled in care management programs experience fewer hospitalizations or readmissions.
Post-Discharge Follow-Up: Evaluating whether enrolled patients demonstrate improved follow-up rates after hospital discharge.
Clinical and Financial Outcomes: Quantifying measurable improvements in patient care, healthcare utilization, and program performance.
The ideal candidate will bring deep expertise in causal inference methodologies, observational healthcare data, and advanced statistical modeling, with the ability to independently lead a high-impact analytical initiative.
This position offers significant flexibility, beginning at approximately 10–15 hours per week, with the opportunity to ramp up hours as the project progresses.
Key Responsibilities
Clinical Outcomes Research & Study Design
Lead the development and execution of longitudinal, quasi-experimental clinical outcomes studies.
Evaluate the effectiveness of APCM and CCM programs using healthcare claims and Electronic Health Record (EHR) data.
Establish appropriate patient cohorts, comparison groups, outcome definitions, and analytical frameworks.
Quantify differences in hospitalization rates, readmissions, and post-discharge follow-up outcomes.
Develop statistically rigorous, reproducible methodologies capable of withstanding actuarial and clinical review.
Causal Inference & Advanced Statistical Modeling
Apply advanced causal inference techniques to estimate the impact of healthcare interventions.
Utilize methodologies such as propensity score matching, inverse probability weighting, difference-in-differences, and other appropriate quasi-experimental approaches.
Identify and address confounding variables, selection bias, and other limitations associated with observational healthcare data.
Develop high-dimensional statistical matching approaches for large, complex datasets.
Conduct sensitivity analyses and validate the robustness of research findings.
Healthcare Data Analysis & Technical Execution
Analyze complex healthcare claims, EHR, and patient utilization datasets.
Develop and maintain analytical workflows using Python, R, and SQL.
Work within existing data environments that may include dbt, Amazon Redshift, and Looker.
Ensure data integrity, analytical reproducibility, and compliance with applicable PHI security requirements.
Produce well-documented analytical code and reporting assets for internal stakeholders.
Stakeholder Collaboration & Reporting
Collaborate directly with the Director of Business Intelligence and other internal stakeholders.
Engage with healthcare clients, clinical leadership, and actuarial teams to align research methodologies and outcome measures.
Present statistical findings and clinical outcomes in a clear, actionable format.
Develop executive-ready presentations, reports, and supporting documentation.
Translate complex analytical findings into meaningful business and clinical insights.
Vendor Deliverable Review & Knowledge Transfer
Review and assess analytical deliverables produced by previous external vendors.
Identify usable methodologies, data assets, and findings that can be incorporated into ongoing research.
Recommend improvements to existing analytical processes and study designs.
Document methodologies and transfer knowledge to the internal Business Intelligence team.
Required Qualifications
Extensive professional experience in data science, biostatistics, epidemiology, health economics, or clinical outcomes research.
Demonstrated expertise in causal inference and observational study design.
Proven experience evaluating healthcare interventions and measuring clinical outcomes.
Strong proficiency in Python, R, and SQL.
Experience analyzing large-scale healthcare claims and EHR datasets.
Deep understanding of propensity score matching, quasi-experimental research methodologies, statistical validation, and confounding adjustment.
Ability to independently design and execute technically sophisticated analytical studies.
Experience developing statistical methodologies suitable for rigorous clinical, actuarial, or external stakeholder review.
Strong understanding of healthcare data privacy, HIPAA, and protected health information requirements.
Excellent communication skills, including the ability to present complex findings to nontechnical stakeholders.
Advanced degree in Statistics, Data Science, Biostatistics, Epidemiology, Health Economics, or a closely related quantitative discipline.
Preferred Qualifications
Previous experience as a Health Economics and Outcomes Research (HEOR) Consultant, Principal Data Scientist, or Lead Biostatistician.
Experience evaluating chronic care management, population health, value-based care, or Medicare-related programs.
Familiarity with APCM, CCM, and healthcare reimbursement models.
Experience working with healthcare actuaries or validating analytical methodologies for actuarial review.
Knowledge of dbt, Amazon Redshift, and Looker.
Experience supporting health-tech startups or rapidly growing healthcare technology companies.
Strong consulting background with the ability to deliver results independently.
Experience developing executive-level clinical outcomes presentations and client-facing research reports.
Contract Details & Compensation
Hourly Rate: $150 per hour.
Engagement Type: 1099 independent contractor or W-2 contract.
Initial Commitment: Approximately 10–15 hours per week.
Growth Opportunity: Potential to increase weekly hours as project requirements evolve.
Work Arrangement: Fully remote.
Schedule: Flexible, with availability for periodic stakeholder meetings and project discussions.
Duration: Project-based engagement with the possibility of extension.
This opportunity is particularly well suited for an experienced consultant seeking a flexible, part-time engagement while contributing to meaningful healthcare research and measurable improvements in patient outcomes.
This position is responsible for complying with all organizational security, privacy, and compliance policies applicable to the role, including separation of duties requirements, and for protecting the confidentiality, integrity, and availability of the information and systems they access.
Equal Employment Opportunity:
Phamily is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. Employment decisions are made without regard to race, color, religion, sex, national origin, age, disability, genetics, veteran status, sexual orientation, gender identity or expression, or any other legally protected status.
Lead Data Scientist – Causal Inference & Clinical Outcomes (Contract)
Location: Remote – United States
Employment Type: Part-Time Contract (1099 Independent Contractor or W-2)
Schedule: 10–15 hours per week initially, with the opportunity to increase hours based on project needs
Compensation: $150 per hour
Reports To: CFO
Engagement Duration: Project-based, with potential for extension or expanded responsibilities
About Jaan Health / Phamily
Jaan Health is an innovative, AI-powered healthcare technology company focused on transforming proactive care management and improving patient outcomes.
Through its proprietary Phamily platform, Jaan Health enables healthcare providers, medical groups, health systems, and Accountable Care Organizations (ACOs) to deliver scalable, high-quality care management services.
Phamily leverages advanced technology and AI-enabled workflows to help providers manage chronic conditions, improve patient engagement, reduce avoidable healthcare utilization, and deliver measurable clinical and financial outcomes.
Learn more at phamily.com.
Position Overview
Jaan Health is seeking a highly experienced Lead Data Scientist specializing in Causal Inference and Clinical Outcomes Research for a flexible, remote consulting engagement.
This opportunity is ideal for a senior-level data scientist, biostatistician, health economist, or Healthcare Economics and Outcomes Research (HEOR) professional who has extensive experience evaluating the impact of healthcare interventions using real-world clinical data.
The consultant will lead the design and execution of a rigorous clinical outcomes study evaluating the effectiveness of Jaan Health's Advanced Primary Care Management (APCM) and Chronic Care Management (CCM) programs.
The primary objective is to establish statistically defensible evidence of program effectiveness, with particular emphasis on:
Hospitalization and Readmission Rates: Determining whether patients enrolled in care management programs experience fewer hospitalizations or readmissions.
Post-Discharge Follow-Up: Evaluating whether enrolled patients demonstrate improved follow-up rates after hospital discharge.
Clinical and Financial Outcomes: Quantifying measurable improvements in patient care, healthcare utilization, and program performance.
The ideal candidate will bring deep expertise in causal inference methodologies, observational healthcare data, and advanced statistical modeling, with the ability to independently lead a high-impact analytical initiative.
This position offers significant flexibility, beginning at approximately 10–15 hours per week, with the opportunity to ramp up hours as the project progresses.
Key Responsibilities
Clinical Outcomes Research & Study Design
Lead the development and execution of longitudinal, quasi-experimental clinical outcomes studies.
Evaluate the effectiveness of APCM and CCM programs using healthcare claims and Electronic Health Record (EHR) data.
Establish appropriate patient cohorts, comparison groups, outcome definitions, and analytical frameworks.
Quantify differences in hospitalization rates, readmissions, and post-discharge follow-up outcomes.
Develop statistically rigorous, reproducible methodologies capable of withstanding actuarial and clinical review.
Causal Inference & Advanced Statistical Modeling
Apply advanced causal inference techniques to estimate the impact of healthcare interventions.
Utilize methodologies such as propensity score matching, inverse probability weighting, difference-in-differences, and other appropriate quasi-experimental approaches.
Identify and address confounding variables, selection bias, and other limitations associated with observational healthcare data.
Develop high-dimensional statistical matching approaches for large, complex datasets.
Conduct sensitivity analyses and validate the robustness of research findings.
Healthcare Data Analysis & Technical Execution
Analyze complex healthcare claims, EHR, and patient utilization datasets.
Develop and maintain analytical workflows using Python, R, and SQL.
Work within existing data environments that may include dbt, Amazon Redshift, and Looker.
Ensure data integrity, analytical reproducibility, and compliance with applicable PHI security requirements.
Produce well-documented analytical code and reporting assets for internal stakeholders.
Stakeholder Collaboration & Reporting
Collaborate directly with the Director of Business Intelligence and other internal stakeholders.
Engage with healthcare clients, clinical leadership, and actuarial teams to align research methodologies and outcome measures.
Present statistical findings and clinical outcomes in a clear, actionable format.
Develop executive-ready presentations, reports, and supporting documentation.
Translate complex analytical findings into meaningful business and clinical insights.
Vendor Deliverable Review & Knowledge Transfer
Review and assess analytical deliverables produced by previous external vendors.
Identify usable methodologies, data assets, and findings that can be incorporated into ongoing research.
Recommend improvements to existing analytical processes and study designs.
Document methodologies and transfer knowledge to the internal Business Intelligence team.
Required Qualifications
Extensive professional experience in data science, biostatistics, epidemiology, health economics, or clinical outcomes research.
Demonstrated expertise in causal inference and observational study design.
Proven experience evaluating healthcare interventions and measuring clinical outcomes.
Strong proficiency in Python, R, and SQL.
Experience analyzing large-scale healthcare claims and EHR datasets.
Deep understanding of propensity score matching, quasi-experimental research methodologies, statistical validation, and confounding adjustment.
Ability to independently design and execute technically sophisticated analytical studies.
Experience developing statistical methodologies suitable for rigorous clinical, actuarial, or external stakeholder review.
Strong understanding of healthcare data privacy, HIPAA, and protected health information requirements.
Excellent communication skills, including the ability to present complex findings to nontechnical stakeholders.
Advanced degree in Statistics, Data Science, Biostatistics, Epidemiology, Health Economics, or a closely related quantitative discipline.
Preferred Qualifications
Previous experience as a Health Economics and Outcomes Research (HEOR) Consultant, Principal Data Scientist, or Lead Biostatistician.
Experience evaluating chronic care management, population health, value-based care, or Medicare-related programs.
Familiarity with APCM, CCM, and healthcare reimbursement models.
Experience working with healthcare actuaries or validating analytical methodologies for actuarial review.
Knowledge of dbt, Amazon Redshift, and Looker.
Experience supporting health-tech startups or rapidly growing healthcare technology companies.
Strong consulting background with the ability to deliver results independently.
Experience developing executive-level clinical outcomes presentations and client-facing research reports.
Contract Details & Compensation
Hourly Rate: $150 per hour.
Engagement Type: 1099 independent contractor or W-2 contract.
Initial Commitment: Approximately 10–15 hours per week.
Growth Opportunity: Potential to increase weekly hours as project requirements evolve.
Work Arrangement: Fully remote.
Schedule: Flexible, with availability for periodic stakeholder meetings and project discussions.
Duration: Project-based engagement with the possibility of extension.
This opportunity is particularly well suited for an experienced consultant seeking a flexible, part-time engagement while contributing to meaningful healthcare research and measurable improvements in patient outcomes.
This position is responsible for complying with all organizational security, privacy, and compliance policies applicable to the role, including separation of duties requirements, and for protecting the confidentiality, integrity, and availability of the information and systems they access.
Equal Employment Opportunity:
Phamily is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. Employment decisions are made without regard to race, color, religion, sex, national origin, age, disability, genetics, veteran status, sexual orientation, gender identity or expression, or any other legally protected status.
Clinical Data Scientist Jobs by Experience Level
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Find Clinical Data Scientist JobsClinical Data Scientist Job Market
Who's Hiring
What Employers Look For
The qualifications that appear most often in clinical data scientist jobs.
- Advanced degree in biostatistics, data science, epidemiology, or a related quantitative field
- Proficiency in SAS, R, or Python for statistical analysis and data manipulation
- Experience with CDISC standards including SDTM and ADaM dataset structures
- Familiarity with clinical trial design, protocols, and regulatory submission requirements
- Ability to collaborate with biostatisticians, clinical operations, and regulatory affairs teams
- Experience using electronic data capture systems and clinical data management platforms
Tips for Your Clinical Data Scientist Job Search
Tailor your resume to trial phases
Hiring managers in clinical settings scan for phase experience, so list Phase I, II, or III work explicitly. Vague claims like 'clinical experience' get passed over when reviewers are matching candidates to a specific trial stage.
Highlight statistical software by context
Don't just list SAS, R, or Python. Name the actual task: survival analysis in R, mixed-effects models in SAS, or ETL pipelines in Python. Reviewers parsing dozens of resumes respond to specificity over software name-dropping.
Apply early to roles that fit
Migrate Mate lists clinical data scientist openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Target postings that name your therapeutic area
Oncology, rare disease, and cardiovascular teams often want domain-specific data scientists, not generalists. Filter for openings that name your therapeutic area and position yourself as already fluent in that disease landscape.
Prepare a programming exercise before interviews
Many clinical data scientist interviews include a live or take-home coding task, often involving data cleaning, CDISC SDTM mapping, or exploratory analysis. Refresh your workflow on messy clinical datasets before any first-round technical screen.
Negotiate scope, not just title
When you receive an offer, ask which trial programs or data domains you'd own from day one. Titles vary wildly across companies, so the actual scope of responsibility matters more than the grade level for long-term career growth.
Clinical Data Scientist Jobs: Frequently Asked Questions
Which companies are hiring the most clinical data scientists?
Xenon7, Phamily, and Latica are hiring the most clinical data scientists right now, with openings concentrated in Illinois, Ohio, and Texas, based on current listings on Migrate Mate as of November 2026. Demand is highest at large pharmaceutical companies and contract research organizations running active late-phase trials.
How many clinical data scientist jobs are remote?
About 100% of clinical data scientist openings are fully remote or hybrid as of November 2026, reflecting the data-centric nature of the role. Positions focused on real-world evidence, statistical programming, and data standards tend to offer the most location flexibility, while roles tied to on-site clinical operations are more likely to require in-person presence.
How do you become a clinical data scientist?
Start with a graduate degree in biostatistics, epidemiology, data science, or a closely related quantitative field. Build hands-on experience with clinical datasets, CDISC standards, and at least one major statistical programming language. Seek internships or entry-level roles at contract research organizations or health systems to gain trial exposure, then move toward more complex analytical responsibilities as you develop domain expertise.
How do you get hired as a clinical data scientist with little experience?
Focus on roles labeled associate or junior clinical data scientist, which are designed for candidates earlier in their careers. Demonstrate academic project work involving real clinical or health datasets, even if not from a commercial trial. Certifications in SAS programming or clinical data management add credibility. Applying to contract research organizations is often a faster path in than applying directly to large pharma companies.
What does the clinical data scientist interview process look like?
Most processes start with a recruiter screen, followed by a technical interview covering statistical methods, data wrangling, and your familiarity with CDISC standards. A take-home or live coding exercise involving clinical data is common at mid-stage. Final rounds typically include a panel with biostatisticians, clinical operations leads, or a hiring manager who evaluates your ability to communicate analytical findings to non-technical stakeholders.
Where can I find and apply to clinical data scientist jobs?
You can find and apply to clinical data scientist jobs on Migrate Mate, which lists current openings from across the United States. Search for roles that match your experience level, therapeutic area, and preferred work arrangement, then apply directly to each listing that fits.
See All 11 Clinical Data Scientist Jobs
Find roles that match your experience and apply in just a few clicks.
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