Director, Commercial Data Science & AI/ML — Oncology

Role Summary

You will lead high-impact data science and AI/ML initiatives that drive commercial strategy across our Oncology portfolio, spanning solid tumors and hematology. Working closely with commercial, medical, market access, and technology teams, you will transform complex oncology data into actionable insights that shape go-to-market decisions, optimize HCP engagement, and accelerate patient access. You will mentor and grow the team, promoting responsible AI practices, with visibility and leadership influence aligned to the mission of uniting science, technology, and talent to get ahead of disease together. Location: Durham Blackwell Street, Philadelphia, PA, USA.

Responsibilities

• Lead the design, development, and delivery of advanced predictive models and AI/ML solutions that support commercial decisions across the Oncology portfolio, including launch readiness, tumor market segmentation, promotional response, and end-to-end patient journey analytics across solid tumors and hematology indications.
• Partner with Commercial, Market Access, Medical Affairs, and Marketing teams to translate business questions into analytical plans with measurable impact on revenue, patient outcomes, and market share within highly competitive oncology markets.
• Build, validate, and operationalize end-to-end machine learning workflows — from data ingestion and feature engineering through model deployment, monitoring, and performance tracking — leveraging oncology commercial data assets such as claims, EMR, specialty pharmacy, and oncology-specific registries.
• Develop and apply AI/ML methods to oncology-specific commercial challenges, including HCP targeting and segmentation by tumor type and treatment line, biosimilar and competitive entry modeling, patient identification and treatment gap analysis, and access barrier identification across complex payer, IDN, and GPO landscapes.
• Lead sophisticated market access analytics including payer mix modeling, formulary coverage impact analysis, net price optimization, and prior authorization burden quantification specific to oncology reimbursement dynamics.
• Lead and mentor a team of data scientists and analysts, setting technical standards, fostering reproducible and responsible AI practices, and building a high-performing, collaborative team culture.
• Communicate complex analytical findings clearly and persuasively to senior commercial and medical leaders, enabling evidence-based decisions on strategy, investment, and resource allocation in the oncology business unit.
• Champion the adoption of modern AI/ML tools, GenAI applications, and scalable data infrastructure to continuously elevate the commercial analytics capability across the oncology organization.
• Collaborate with IT, Data Engineering, and external vendors to ensure data quality, governance, and compliance with relevant privacy and regulatory standards (e.g., HIPAA, GDPR, FDA promotional guidelines).

Qualifications

• Required: Advanced degree (Master's or PhD) in Data Science, Computer Science, Statistics, Applied Mathematics, or a related quantitative field.
• Required: 10+ years of hands-on experience in applied data science, machine learning, or statistical modeling, with at least 3 years in a pharmaceutical or biotech commercial setting focused on oncology.
• Required: Demonstrated experience working with oncology commercial data assets, including IQVIA (LAAD, DDD, Xponent), Optum (Clinformatics, claims data), Symphony Health, or similar syndicated and patient-level data sources, with the ability to assess data quality, coverage, and appropriate use cases for each.
• Required: Strong programming skills in Python or R, experience with relevant ML libraries (e.g., scikit-learn, TensorFlow, PyTorch, XGBoost), and demonstrated ability to leverage AI-powered development tools (e.g., GitHub Copilot, Cursor, or LLM-based coding agents) to accelerate and enhance programming workflows.
• Required: Experience deploying models and building end-to-end ML pipelines using cloud platforms (AWS, Azure, or GCP) or containerized services.
• Required: Proven ability to lead complex, cross-functional commercial analytics projects and influence senior stakeholders in a matrixed oncology organization.
• Required: Excellent written and verbal communication skills with the ability to translate complex analytical outputs into clear commercial recommendations for oncology business leaders.
• Preferred: PhD in a quantitative, life science, health economics, or oncology-related discipline.
• Preferred: Deep therapeutic area expertise in oncology, with working knowledge of both solid tumor (e.g., lung, breast, colorectal, GU) and hematology (e.g., lymphoma, leukemia, myeloma) commercial landscapes.
• Preferred: Experience modeling biosimilar or competitive entry dynamics in oncology, including price erosion, formulary switching, and account-level impact forecasting.
• Preferred: Familiarity with NLP or large language models applied to oncology commercial use cases, such as call note analysis, HCP sentiment mining, tumor board insights extraction, or medical affairs literature synthesis.
• Preferred: Experience with MLOps practices including feature stores, model governance frameworks, and automated monitoring in a regulated pharmaceutical environment.
• Preferred: Track record of building and developing high-performing data science teams in a global, commercial pharma environment.
• Preferred: Knowledge of oncology-specific compliance frameworks governing commercial data use, including HIPAA, PhRMA Code, and FDA promotional guidelines for oncology products.

Skills

• Proficiency in Python or R and ML libraries (scikit-learn, TensorFlow, PyTorch, XGBoost); ability to leverage AI-powered development tools.
• Experience deploying models and building end-to-end ML pipelines on cloud platforms (AWS, Azure, GCP) or containerized environments; familiarity with MLOps practices.
• Strong leadership and collaboration capabilities; proven ability to lead cross-functional teams and influence senior stakeholders.
• Knowledge of oncology data assets (IQVIA, Optum, Symphony Health) and data quality assessment; understanding of payer, IDN, and GPO landscapes.
• Excellent communication skills to translate complex analytics into clear business recommendations for oncology leaders.
• Familiarity with privacy and regulatory standards (HIPAA, GDPR, FDA guidelines) and data governance best practices.
• Interest and experience in adopting GenAI and innovative analytics tools to elevate commercial analytics capabilities.