Director Oncology Data Science LMW

Role Summary

The Director of Oncology Data Science will lead a global team of data scientists focused on Low Molecular Weight (LMW) drug development within the Oncology Disease Area. The role involves guiding the LMW data science function, collaborating with Oncology stakeholders and Biomedical Research, and delivering innovative data-driven solutions to enhance the LMW portfolio and patient outcomes.

Responsibilities

• Lead and develop a global team of US and Europe-based data scientists, driving high-quality data science grounded in biology and applicable methodologies.
• Co-develop and execute a data science strategy to enable and enhance innovation in the LMW drug development space.
• Align with Oncology Drug Development teams to represent the LMW data science function and embed data science in drug development teams.
• Interface with Oncology Disease Area leaders to align priorities and deploy resources effectively.
• Collaborate with the Oncology Data Science AI team and other groups (e.g., generative chemistry) on developing ML algorithms, AI models, and platforms for predictive and prescriptive insights.
• Engage with stakeholders in IT, Chemistry, and Technology to build partnerships and shared projects.
• As part of the Oncology Data Science Leadership team, contribute to overall direction and manage the LMW portfolio.
• Collaborate with Oncology Data Science engineering teams to ensure data standards, governance, and reproducibility in research.
• Lead matrix teams such as Translational Data Science Clusters focusing on disease areas or technologies (genomics, proteomics, spatial technologies, etc.).
• Present and externalize team work in conferences, abstracts, and journals.

Qualifications

• Ph.D. in data science, computational biology, or related fields; strong biological understanding of oncology and LMW drug development.
• 10+ years of experience in a data science role in oncology drug development.
• 5+ years of people management experience in data science.
• Experience with QC and data analysis for assays such as bulk RNA-seq, scRNA-seq, and chromatin-based assays (ChIP-seq, ATAC-seq, Hi-C).
• Strong expertise in quantitative/computational biology, AI/ML, and statistical modeling with a track record of delivering data-driven insights.
• Excellent communication skills to convey complex data insights to cross-functional teams.
• Proven track record of contributions to the scientific community (papers and/or conference presentations).

Education

• Ph.D. in data science, computational biology or related fields.

Additional Requirements

• Location: Cambridge, MA site; remote work not available; 10% travel required.