Data Scientist

• Translate approved business questions into testable hypotheses sized at one to three days with clear exit criteria and confidence levels.
• Design and execute analytical methods including segmentation, causal inference, forecasting, propensity modeling, uplift modeling, and experiment design.
• Defend analytical methodology and findings during review and governance forums.
• Provide an initial evidence-based answer within five business days, including inconclusive or negative results.
• Maintain daily progress updates covering hypotheses tested, findings, confidence level, next steps, and blockers.
• Develop production-quality, version-controlled analytical code with reproducible notebooks and documented assumptions.
• Partner with Data Engineers to define features, granularity, historical data requirements, and address data gaps proactively.
• Convert validated findings into metric definitions and business logic for visualization and reporting.
• Collaborate with client data scientists and provide comprehensive model documentation for handover and reuse.

Additional Responsibilities (Senior Data Scientist)

Serve as analytics lead for the pod.

Own analytical quality across engagements.

Act as peer reviewer and mentor internal and client analysts.

Drive analytics best practices and technical excellence.

Data Scientist: 6+ years of applied data science experience.

Advanced proficiency in Python and/or R and SQL.

Demonstrated experience deploying analytical models into production.

Strong foundation in statistics, machine learning, experimentation, and predictive analytics.

Strong background in statistics, mathematics and with data science degree

Healthcare, medical device, life sciences, or regulated-industry experience preferred.