Associate Director, Data Science - Market Access

Role Summary

Associate Director of Data Science - Market Access leads the development and delivery of advanced analytics solutions to support market access and pricing decisions. The role involves performing analyses on patient longitudinal data, creating interactive dashboards, and translating complex data into actionable insights for stakeholders. This position collaborates with multiple departments to support strategic initiatives and enhance data-driven decision-making.

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. 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

• Required: 5+ years of experience in data science or advanced analytics within Pharmaceutical or Payer organizations
• Required: 5+ years of hands-on experience building and deploying predictive models and machine learning solutions on large-scale datasets
• Required: Demonstrated experience working with workflow orchestration tools (Dagster, Airflow, or similar) to productionize analytical models
• Required: Proven track record of translating business problems into data science solutions that drive measurable outcomes
• Required: Experience collaborating with data engineering teams and contributing to data pipeline development
• Required: Advanced proficiency in Python or R for statistical modeling, machine learning, and data analysis
• Required: Experience with ML frameworks (scikit-learn, TensorFlow, PyTorch, XGBoost, etc.) and predictive modeling techniques
• Required: Hands-on experience with workflow orchestration platforms (Dagster, Airflow, Prefect, or similar)
• Required: Proficiency in SQL for complex data manipulation and working with relational databases
• Required: Expertise in data visualization tools (Tableau, Power BI, or similar) and creating executive-level dashboards
• Preferred: Experience with cloud platforms (Kubernetes) and modern data stack technologies
• Preferred: Strong foundation in statistical methods, experimental design, and A/B testing
• Preferred: Understanding of MLOps principles and model deployment best practices
• Preferred: Deep understanding of pharmaceutical market access, pricing strategies, and reimbursement dynamics
• Preferred: Experience analyzing longitudinal patient data, claims data, and formulary datasets
• Preferred: Working knowledge of the US healthcare system, payer landscape, and regulatory environment
• Preferred: Familiarity with healthcare data standards (e.g., NDC, HCPCS, ICD codes, IQVIA)
• Soft Skill: Exceptional problem-solving abilities with a structured, hypothesis-driven approach
• Soft Skill: Strong communication skills with ability to translate complex technical concepts for non-technical stakeholders
• Soft Skill: Proven ability to manage multiple analytical projects simultaneously and meet deadlines
• Soft Skill: Collaborative mindset with experience working across data engineering, product management, and business teams
• Soft Skill: Detail-oriented with strong organizational and project management capabilities
• Soft Skill: Self-directed learner who stays current with emerging data science methodologies and technologies
• Soft Skill: Ability to mentor and provide technical guidance to developers and junior analysts

Skills

• Python or R for statistical modeling, machine learning, and data analysis
• ML frameworks (scikit-learn, TensorFlow, PyTorch, XGBoost, etc.) and predictive modeling techniques
• Workflow orchestration platforms (Dagster, Airflow, Prefect, or similar)
• SQL for complex data manipulation and working with relational databases
• Data visualization tools (Tableau, Power BI, or similar) and creating executive-level dashboards
• Cloud platforms (Kubernetes) and modern data stack technologies
• Statistical methods, experimental design, and A/B testing
• MLOps principles and model deployment best practices
• Pharmaceutical market access, pricing strategies, and reimbursement dynamics
• Longitudinal patient data, claims data, and formulary datasets
• Understanding of the US healthcare system, payer landscape, and regulatory environment
• Healthcare data standards (NDC, HCPCS, ICD codes, IQVIA)
• Strong communication, collaboration, and project management

Education

• BA or BS Degree
• Advanced Degree