Use enterprise data to build decision-ready intelligence across Empower Pharmacy’s regulated 503A/503B environment.

Translate ambiguous business questions into clear analytical problems, define success measures, and select methods that produce actionable outputs.
Create reliable analytical datasets by assessing data quality, resolving inconsistencies, documenting assumptions, and maintaining traceability from source through final analysis.
Develop, evaluate, and refine statistical or predictive models using techniques that balance performance, interpretability, robustness, and business needs.
Design rigorous analyses and experiments to test hypotheses, quantify impact, and separate meaningful signal from noise before operational decisions.
Communicate results, uncertainty, tradeoffs, and recommended actions clearly to cross-functional partners to support timely, evidence-based decisions.
Use AI-assisted analytical techniques when appropriate to accelerate pattern identification and scenario evaluation, while validating outputs and preserving accountable human judgment.
Validate methods, outputs, and assumptions through repeatable checks, documentation, and peer review practices that improve accuracy and trust.
Perform data science with disciplined attention to privacy, patient safety, documentation, and applicable controls within regulated 503A and 503B operations.
Identify recurring analytical bottlenecks, improve reusable methods and workflows, and share practical knowledge to increase team consistency and speed.

Statistical and modeling depth: Strong statistical reasoning, experimental design, predictive modeling, and analytical validation with sound method selection judgment.
Data preparation rigor: Ability to prepare, assess, and interpret complex datasets while maintaining data quality, traceability, privacy, and documentation standards.
Stakeholder communication: Clear communication skills to translate technical findings, uncertainty, and tradeoffs into practical recommendations for partners with varied backgrounds.
AI-enabled analytics: Working knowledge of AI-enabled analytical techniques for pattern identification or scenario evaluation, including validation practices that support transparent and accountable decisions.

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 for operational decision-making.
Bachelor’s degree in a quantitative or related discipline, or an equivalent combination of education and experience demonstrating comparable analytical capability.
Experience in a regulated, quality-sensitive, or operationally complex environment where analytical accuracy, documentation, privacy, and sound judgment materially impact outcomes.
Experience partnering across business and technical functions to convert 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.

Required to talk and hear while performing job responsibilities.
Often required to remain in a stationary position for a significant amount of the workday.
Frequently uses hands and fingers to access, input, and retrieve information from a computer and other office productivity devices.
Regularly required to move about the office and around the corporate campus.
Regularly required to stand, walk, reach with arms and hands, climb or balance, and to stoop, kneel, crouch or crawl.

Data Scientist

Job Description