Jobs at Empower Pharmacy (Now Hiring) — 81 open — Page 4

Position Summary:

The Staff Data Scientist drives enterprise impact by translating complex data into scalable, decision-ready intelligence that accelerates growth, improves quality, and optimizes regulated operations. This role owns end-to-end data science initiatives across functions, shaping strategy, execution, and measurable outcomes while influencing senior leadership decisions. Leveraging AI as a force multiplier, the role increases speed, scale, and precision of insights, enabling superior forecasting, experimentation, and optimization. Operating within Empower’s hyper-growth 503A/503B environment, this position ensures compliant, high-quality, and auditable analytics at scale. The role demonstrates exceptional strategic thinking, rigorous execution, and rapid learning agility to navigate ambiguity, challenge assumptions, and continuously elevate enterprise data science capabilities and business performance.

Responsibilities:

Enterprise Data Science

• Strategic Ownership: Lead high-impact, cross-functional data science initiatives from problem framing through deployment, ensuring alignment with enterprise priorities and delivering measurable business outcomes.
• Solution Design: Design advanced analytical models, experimentation frameworks, and optimization strategies that translate ambiguous business challenges into structured, high-value solutions.
• Impact Delivery: Embed data science into operational workflows, ensuring solutions are actionable, interpretable, sustainable, and continuously improved through feedback loops, monitoring, and AI-enabled automation.

Advanced Analytics Execution

• Model Development: Develop and deploy sophisticated models, including forecasting, causal inference, and optimization, ensuring technical rigor and business relevance.
• Experimentation Design: Establish robust experimentation and measurement frameworks, defining KPIs and success criteria that enable confident, data-driven decisions.
• Data Utilization: Transform complex datasets into high-quality analytical assets, ensuring data integrity, governance, and usability while applying AI-driven feature engineering and data processing techniques.

Stakeholder Influence and Innovation

• Executive Partnership: Partner with senior leaders to identify opportunities, challenge assumptions, and shape data-driven strategies that align with business objectives and regulatory expectations.
• Insight Communication: Communicate complex analytical results through compelling narratives and visualizations, ensuring clarity, trust, and adoption.
• Capability Building: Elevate organizational data science maturity by mentoring peers, promoting best practices, and introducing innovative methodologies and AI capabilities.

Knowledge and Skills:

• Advanced expertise in Python, SQL, and modern data platforms, with strong capabilities in forecasting, experimentation, optimization, and causal inference.
• Deep understanding of AI and machine learning techniques and their application to improve speed, scale, decision quality, and automation within complex, regulated environments.
• Strong business acumen with the ability to translate ambiguity into structured analytical approaches and communicate insights effectively to executive stakeholders.
• Proficiency in designing KPIs, measurement frameworks, and scalable analytics solutions that ensure data integrity, governance, and operational impact.

Experience and Qualifications:

• Minimum of 8 years of experience in data science, advanced analytics, statistics, operations research, or a related quantitative field, including ownership of large-scale or cross-functional initiatives.
• Bachelor’s degree in Statistics, Data Science, Applied Mathematics, Economics, Computer Science, Engineering, or a related discipline required; master’s degree preferred; PhD is a plus.
• Strong strategic thinking, executive communication, cross-functional collaboration, stakeholder influence, and the ability to solve complex problems in ambiguous environments.
• 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.
• Preferred certification in statistics, experimentation, forecasting, optimization, causal inference, or a related analytical discipline.