Data Scientist

Position Summary:
The Data Scientist drives business impact by translating complex enterprise data into trusted, decision-ready intelligence that improves quality, accelerates growth, and optimizes operations across Empower’s hyper-growth 503A/503B environment. This role executes end-to-end data science initiatives, from problem framing and data preparation through model development, validation, interpretation, and communication, delivering scalable analytical solutions that support measurable business outcomes. Leveraging AI as a force multiplier, the role increases the speed, scale, and precision of analysis while applying strong statistical judgment, rigorous experimentation, and disciplined validation. Partnering cross-functionally, the Data Scientist identifies patterns, quantifies opportunities, evaluates tradeoffs, and translates findings into actionable insights that enable sound execution. The role demonstrates strategic thinking, rigorous execution, and learning agility to navigate evolving priorities, challenge assumptions, and continuously strengthen analytical capabilities while maintaining privacy, traceability, compliance, patient safety, and human accountability.

Responsibilities:
Analytical Ownership

Problem Framing: Translate ambiguous business questions into precise analytical problems, define success measures, and select methods that produce actionable, decision-ready outputs.
Data Preparation: Build reliable analytical datasets by assessing data quality, resolving inconsistencies, documenting assumptions, and preserving traceability from source through final analysis.
Model Development: Develop and evaluate statistical or predictive models using appropriate techniques, balancing performance, interpretability, robustness, and practical business requirements.
Business Partnership

Insight Delivery: Communicate findings, uncertainty, tradeoffs, and recommended actions clearly to cross-functional partners, enabling timely decisions grounded in evidence and business context.
Experiment Design: Design rigorous analyses and experiments that test hypotheses, quantify impact, and distinguish meaningful signals from noise before operational decisions are made.
Decision Support: Apply AI-assisted analytical techniques where appropriate to accelerate pattern identification and scenario evaluation while validating outputs and preserving accountable human judgment.
Regulated Excellence

Quality Controls: Validate analytical methods, outputs, and assumptions through repeatable checks, documentation, and peer review practices that strengthen accuracy and trust.
Compliance Alignment: Conduct data science work with disciplined attention to privacy, patient safety, documentation, and applicable controls within regulated 503A and 503B operations.
Process Improvement: Identify recurring analytical bottlenecks, improve reusable methods and workflows, and share practical knowledge that increases team consistency, speed, and analytical maturity.

Knowledge and Skills:
Strong statistical reasoning, experimental design, predictive modeling, and analytical validation skills, with sound judgment in selecting methods appropriate to business questions.
Ability to prepare, assess, and interpret complex datasets while maintaining data quality, traceability, privacy, and documentation standards in regulated operations.
Clear communication skills for translating technical findings, uncertainty, and tradeoffs into practical recommendations for cross-functional partners with varied analytical backgrounds.
Working knowledge of AI-enabled analytical techniques for pattern identification or scenario evaluation, including validation practices that preserve transparent and accountable decision-making.

Experience and Qualifications:
Minimum 5 years experience applying data science, statistical analysis, or predictive modeling to complex business problems with measurable outcomes and documented analytical rigor.
Practical experience preparing data, evaluating model performance, testing hypotheses, and communicating findings to stakeholders who use analysis to make operational decisions.
Bachelor’s degree in a quantitative or related discipline, or an equivalent combination of education and practical experience demonstrating comparable analytical capability.
Experience working in a regulated, quality-sensitive, or operationally complex environment where analytical accuracy, documentation, privacy, and sound judgment materially affect outcomes.
Experience partnering across business and technical functions to translate ambiguous questions into structured analyses, reusable methods, and decision-ready recommendations.
Deep technical expertise in Python, SQL, experimentation, causal inference, forecasting, optimization, KPI and measurement framework design, decision-support analytics, and modern data platforms such as Snowflake, AWS, and Microsoft Azure.