Director, Commercial Data Science Location: New Jersey Industry: Life Sciences / Biopharma Function: Commercial Data Science & Analytics Contract Position Job Summary The Director of Data Science will lead the application of advanced analytics, artificial intelligence, and machine learning to support commercial strategy and product initiatives. This leader will serve as a technical and strategic resource across the organization, with responsibility for leveraging the commercial data environment, predictive modeling, and emerging AI capabilities to generate actionable business insights.
The role
will partner closely with Medical Affairs, Sales, Market Access, Marketing, and other cross-functional stakeholders to develop data-driven solutions that support product launches and ongoing commercial activities. The Director will also help establish scalable systems, analytical capabilities, and AI-enabled tools that improve decision-making, operational efficiency, and the organization’s ability to identify and capitalize on market opportunities.
Key Responsibilities
Lead and develop a high-performing data science team, providing technical direction, mentorship, and alignment with the organization’s long-term objectives.
Act as a strategic liaison between the IT organization and Insights & Analytics, helping translate business priorities into scalable data and technology solutions.
Partner with Brand and cross-functional business teams to take analytical initiatives from initial concept and hypothesis through execution, validation, and delivery.
Leverage commercial and healthcare data to uncover growth opportunities, anticipate market challenges, and inform strategic decisions related to products and indications.
Develop and evaluate predictive models and analytical methodologies to identify high-value healthcare providers and organizations based on prescribing potential and other relevant commercial factors.
Lead efforts to bring together information from multiple sources, including claims, CRM, payer, digital, market research, and external data, to create comprehensive analytical solutions.
Establish and refine commercial performance metrics and KPIs that support both product launch planning and ongoing post-launch decision-making.
Drive the development of AI-enabled applications that improve how Sales and Medical Affairs teams engage with healthcare providers, including provider intelligence, recommended actions, automated briefings, and natural-language summaries.
Promote the practical adoption of generative AI and other AI technologies to increase the efficiency of analytical workflows, including exploratory analysis, statistical modeling, programming, reporting, and technical documentation.
Establish best practices, standards, and appropriate safeguards for incorporating AI into data science processes while helping the team develop stronger AI-assisted analytics capabilities.
Qualifications
BS degree in a quantitative discipline, such as statistics, operations research, econometrics, computer science, engineering, or a related field; or MS/PhD in a quantitative discipline with relevant working experience.
At least 7 years of related work experience; 4+ years with an MS/PhD.
Strong experience leveraging AI to develop and deliver data analysis and data science work. Must have direct, hands-on experience building with Claude or a similar AI platform, including agentic workflows, AI-assisted coding, prompt engineering, or related applications, to accelerate modeling, analysis, and reporting.
Track record of embedding AI/ML tools and generative AI copilots into the data science lifecycle to improve the speed and quality of insight delivery to business stakeholders.
People management experience, including direct or indirect reports.
Experience leading cross-functional delivery and influencing executive stakeholders.
Demonstrated ability to tell data-driven stories that allow teams and/or companies to develop internal strategy based on clear insights and recommendations.
Proven ability to develop and deliver a product roadmap.
Expert-level proficiency in Python, R, SQL, and at least one modern ML framework.
Experience with cloud platforms and APIs.
Deep experience with data stack components, including SQL warehouses such as Snowflake, Databricks, or Redshift; orchestration tools such as Airflow or dbt; and BI platforms such as Tableau or Power BI.
Strong knowledge of biopharma data and compliance considerations.