Associate Director, Data Science - Market Access

Role Summary

Associate Director of Data Science, Market Access. Location: Cambridge, MA; Morristown, NJ. Lead the development and delivery of advanced analytics to support market access and pricing decisions in a pharmaceutical setting. Analyze patient longitudinal data, build dashboards, and translate complex data into actionable insights. Collaborate with cross-functional teams to drive data-driven decision-making.

Responsibilities

• Design, develop, and deploy predictive models and analytical solutions using Dagster, Airflow, and DBT workflows to inform market access and pricing decisions. Hands-on experience with R and/or Python is required.
• 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

Qualifications

• 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
• Demonstrated experience working with workflow orchestration tools (Dagster, Airflow, or similar) to productionize analytical models
• Proven track record of translating business problems into data science solutions that drive measurable outcomes
• Experience collaborating with data engineering teams and contributing to data pipeline development
• 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)
• 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

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
• Understanding of MLOps principles and model deployment best practices

Education

• BA or BS Degree
• Advanced Degree