Associate Director, Data Science - Market Access
Scorpion Therapeutics, Cambridge, Massachusetts, us, 02140
Role Summary
Associate Director of Data Science in Market Access, leading the development and delivery of advanced analytics to support market access and pricing decisions. Design and oversee analyses of patient longitudinal data, create interactive dashboards, and translate complex data into actionable insights for stakeholders. Collaborate with cross-functional teams to drive data-driven decision-making and advance predictive capabilities. 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 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
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Associate Director of Data Science in Market Access, leading the development and delivery of advanced analytics to support market access and pricing decisions. Design and oversee analyses of patient longitudinal data, create interactive dashboards, and translate complex data into actionable insights for stakeholders. Collaborate with cross-functional teams to drive data-driven decision-making and advance predictive capabilities. 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 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
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