Associate Director, Data Science - Market Access

Main Responsibilities

• Design, develop, and deploy predictive models and analytical solutions using Dagster/Airflow and DBT workflows to drive data-informed market access and pricing decisions.
• Architect and maintain scalable datasets that integrate with existing data engineering infrastructure and support cross-functional analytical needs.
• Create interactive dashboards and reports using business intelligence tools that translate complex data into actionable insights for stakeholders.
• Perform advanced statistical analysis on patient longitudinal data and large customer datasets to identify trends, patterns, and strategic opportunities.
• Develop and implement machine learning algorithms to enhance forecasting capabilities and predictive analytics across market access functions.
• Collaborate closely with the data engineering team, SQL developers, and analytics product management to ensure data quality, pipeline efficiency, and business alignment.
• Serve as the technical bridge between data engineering infrastructure and business-facing analytics, ensuring seamless integration of analytical solutions.
• Partner cross-functionally with Pricing, Contract Development, Value and Access, Account Management, Finance, Forecasting, and Data Management teams to drive strategic initiatives.
• Communicate complex analytical findings through compelling data narratives and visualizations tailored to diverse audiences.
• Continuously evaluate and implement emerging methodologies and technologies in data science to advance the team's predictive capabilities.

About You

• Experience: 5+ years of experience in data science or advanced analytics within Pharmaceutical or Payer organizations; 5+ years of hands-on experience building and deploying predictive models and machine learning solutions on large-scale datasets.
• Technical Skills: Advanced proficiency in Python or R for statistical modeling, machine learning, and data analysis; experience with ML frameworks (scikit-learn, TensorFlow, PyTorch, XGBoost, etc.) and predictive modeling techniques; hands-on experience with workflow orchestration platforms (Dagster, Airflow, Prefect, or similar); proficiency in SQL for complex data manipulation and working with relational databases; expertise in data visualization tools (Tableau, Power BI, or similar) and creating executive-level dashboards; experience with cloud platforms (Kubernetes) and modern data stack technologies; strong foundation in statistical methods, experimental design, and A/B testing.
• Domain Knowledge: Deep understanding of pharmaceutical market access, pricing strategies, and reimbursement dynamics; experience analyzing longitudinal patient data, claims data, and formulary datasets; working knowledge of the US healthcare system, payer landscape, and regulatory environment; familiarity with healthcare data standards (e.g., NDC, HCPCS, ICD codes, IQVIA).
• Soft Skills: Exceptional problem-solving abilities with a structured, hypothesis-driven approach; strong communication skills with ability to translate complex technical concepts for non-technical stakeholders; proven ability to manage multiple analytical projects simultaneously and meet deadlines; collaborative mindset with experience working across data engineering, product management, and business teams; detail-oriented with strong organizational and project management capabilities; self-directed learner who stays current with emerging data science methodologies and technologies; ability to mentor and provide technical guidance to developers and junior analysts.