Senior Data Scientist

Jobtailor; United States; On-site; USD 140,000 - 205,000; Full time

Job summary

Jobtailor is seeking a senior Data Scientist to lead IDA initiatives and AI-enabled workflows within healthcare analytics. You will design segmentation, define evaluation criteria, and translate business needs into analytical plans while mentoring junior data scientists.

You will collaborate with AI/ML engineers, data engineers, and governance partners to productionize models, monitor performance, ensure privacy, and communicate findings to stakeholders across regulatory and scientific domains.

Qualifications

• Bachelor's degree in Data Science, Statistics, Biostatistics, Computer Science, Clinical Informatics, Engineering, or a related quantitative discipline, with 6+ years of relevant experience; an advanced degree may substitute for a portion of the experience requirement; MBA with 5+ years
• Experience applying data-science methods in healthcare, pharmaceutical, life-sciences, or another regulated environment
• Hands-on experience developing and evaluating statistical, machine-learning, natural-language-processing, or AI-based solutions
• Proficiency with Python or R and SQL, together with experience using modern data-science libraries and analytical development environments
• Experience working with healthcare or pharmaceutical data, such as claims, electronic health records, clinical, scientific, engagement, or real-world data
• Experience defining evaluation frameworks, validating models, and clearly documenting methods, assumptions, limitations, and results
• Ability to independently lead projects or analytical workstreams and influence outcomes in a cross-functional matrixed environment
• Strong communication and interpersonal skills, with the ability to build credibility and explain complex analytical topics to varied audiences
• Understanding of data privacy, governance, security, legal, regulatory, and compliance considerations relevant to healthcare analytics and AI
• Effective English verbal and written communication skills appropriate for scientific, technical, regulatory, and business settings
• Candidates must be authorized to be employed in the U.S. by any employer
• Permanent work authorization in the United States is required
• U.S. work visa sponsorship is not available now or in the future
• Preferred: Master's degree or doctorate in a related field
• Preferred: Experience with large language models, generative AI, agentic systems, or evaluation of AI-enabled workflows
• Preferred: Experience with cloud data and analytics platforms such as Databricks or Snowflake
• Preferred: Familiarity with OMOP or HEDIS
• Preferred: Experience operationalizing models and monitoring performance after deployment
• Preferred: Experience mentoring data scientists or providing technical leadership
• Preferred: Working knowledge of pharmaceutical medicine, clinical development, evidence generation, Medical Affairs, and Good Clinical Practice

Responsibilities

• Lead selection, design, and evaluation of analytical and machine-learning approaches for IDA initiatives and AI-enabled workflows
• Design cohort, customer, and healthcare professional segmentation methodologies using healthcare and real-world data
• Define analytical logic, performance measures, validation methods, and decision criteria for models and agentic workflows
• Conduct advanced analyses to identify patterns, trends, opportunities, and risks informing Medical Affairs decisions
• Document analytical methods, assumptions, limitations, and outputs to ensure reproducibility and fitness for purpose
• Evaluate emerging data-science, artificial-intelligence, and large-language-model techniques
• Prepare, integrate, explore, and analyze complex structured and unstructured datasets
• Develop, test, validate, and refine statistical, machine-learning, natural-language-processing, and AI-based models
• Partner with AI/ML and data engineers to translate methods into production-ready pipelines and workflows
• Collaborate with architecture and governance partners on data models, quality, privacy, and governance
• Establish monitoring for model performance, data drift, output quality, bias, and continued fitness for use
• Provide analytical input to strategy, use-case prioritization, roadmaps, and delivery decisions
• Translate Medical Affairs and business needs into analytical questions, requirements, hypotheses, and evaluation plans
• Communicate findings, recommendations, uncertainty, and trade-offs to technical and nontechnical stakeholders
• Lead projects or analytical workstreams through influence and stakeholder engagement
• Provide technical guidance, mentorship, and coaching to data scientists and analytical colleagues
• Promote data-science standards, reusable methods, peer review, documentation, and knowledge sharing
• Support responsible, transparent, and accountable use of data and AI

Job description

• Lead selection, design, and evaluation of analytical and machine-learning approaches for IDA initiatives and AI-enabled workflows
• Design cohort, customer, and healthcare professional segmentation methodologies using healthcare and real-world data
• Define analytical logic, performance measures, validation methods, and decision criteria for models and agentic workflows
• Conduct advanced analyses to identify patterns, trends, opportunities, and risks informing Medical Affairs decisions
• Document analytical methods, assumptions, limitations, and outputs to ensure reproducibility and fitness for purpose
• Evaluate emerging data-science, artificial-intelligence, and large-language-model techniques
• Prepare, integrate, explore, and analyze complex structured and unstructured datasets
• Develop, test, validate, and refine statistical, machine-learning, natural-language-processing, and AI-based models
• Partner with AI/ML and data engineers to translate methods into production-ready pipelines and workflows
• Collaborate with architecture and governance partners on data models, quality, privacy, and governance
• Establish monitoring for model performance, data drift, output quality, bias, and continued fitness for use
• Provide analytical input to strategy, use-case prioritization, roadmaps, and delivery decisions
• Translate Medical Affairs and business needs into analytical questions, requirements, hypotheses, and evaluation plans
• Communicate findings, recommendations, uncertainty, and trade-offs to technical and nontechnical stakeholders
• Lead projects or analytical workstreams through influence and stakeholder engagement
• Provide technical guidance, mentorship, and coaching to data scientists and analytical colleagues
• Promote data-science standards, reusable methods, peer review, documentation, and knowledge sharing
• Support responsible, transparent, and accountable use of data and AI

Requirements

• Bachelor's degree in Data Science, Statistics, Biostatistics, Computer Science, Clinical Informatics, Engineering, or a related quantitative discipline, with 6+ years of relevant experience; an advanced degree may substitute for a portion of the experience requirement; MBA with 5+ years
• Experience applying data-science methods in healthcare, pharmaceutical, life-sciences, or another regulated environment
• Hands-on experience developing and evaluating statistical, machine-learning, natural-language-processing, or AI-based solutions
• Proficiency with Python or R and SQL, together with experience using modern data-science libraries and analytical development environments
• Experience working with healthcare or pharmaceutical data, such as claims, electronic health records, clinical, scientific, engagement, or real-world data
• Experience defining evaluation frameworks, validating models, and clearly documenting methods, assumptions, limitations, and results
• Ability to independently lead projects or analytical workstreams and influence outcomes in a cross-functional matrixed environment
• Strong communication and interpersonal skills, with the ability to build credibility and explain complex analytical topics to varied audiences
• Understanding of data privacy, governance, security, legal, regulatory, and compliance considerations relevant to healthcare analytics and AI
• Effective English verbal and written communication skills appropriate for scientific, technical, regulatory, and business settings
• Candidates must be authorized to be employed in the U.S. by any employer
• Permanent work authorization in the United States is required
• U.S. work visa sponsorship is not available now or in the future
• Preferred: Master's degree or doctorate in a related field
• Preferred: Experience with large language models, generative AI, agentic systems, or evaluation of AI-enabled workflows
• Preferred: Experience with cloud data and analytics platforms such as Databricks or Snowflake
• Preferred: Familiarity with OMOP or HEDIS
• Preferred: Experience operationalizing models and monitoring performance after deployment
• Preferred: Experience mentoring data scientists or providing technical leadership
• Preferred: Working knowledge of pharmaceutical medicine, clinical development, evidence generation, Medical Affairs, and Good Clinical Practice

Core Competencies

Demonstrates expertise in statistical analysis, machine learning, and AI model development within healthcare analytics, with a strong focus on data governance, privacy, and compliance. Proven ability to lead analytical projects, communicate complex findings, and mentor data science teams effectively.

Highest-signal resume keywords

• Statistical Analysis
• Machine Learning
• Natural Language Processing
• Data Governance
• Healthcare Analytics

Hard Skills

• Python
• R
• SQL
• Statistical Modeling
• Machine Learning Solutions
• Data Analysis
• Model Validation
• Analytical Method Documentation
• AI-Based Solutions
• Evaluation Frameworks

Soft Skills

• Strong Communication Skills
• Interpersonal Skills
• Project Leadership
• Stakeholder Engagement
• Mentorship

Industry Keywords

• Healthcare
• Pharmaceutical
• Life Sciences
• Regulatory Environment
• Data Privacy
• Compliance
• Good Clinical Practice
• Real-World Data
• Electronic Health Records
• Claims Data

Tools & Technologies

• Databricks
• Snowflake
• Data Science Libraries
• Analytical Development Environments