Director Oncology Data Science LMW

Role Summary

Director of Oncology Data Science LMW responsible for leading a global team of data scientists specializing in Low Molecular Weight drug development. The role involves guiding the LMW data science portfolio, collaborating with Oncology stakeholders and Biomedical Research, and delivering innovative data-driven solutions to enhance drug development 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 with aligned scientific direction.
• Interface with Oncology Disease Area leaders to align priorities and effectively deploy resources.
• Collaborate with the Oncology Data Science AI team and generative chemistry teams on development and implementation of ML algorithms, AI models, and platforms for predictive and prescriptive insights.
• Engage with stakeholders across IT, Chemistry, and Technology to build partnerships and shared projects.
• As a member of the Oncology Data Science Leadership team, contribute to overall direction and management, representing the LMW portfolio.
• Collaborate with Oncology Data Science engineering teams to ensure adherence to data standards, governance, and strategy, promoting reproducibility in research.
• Lead matrix teams such as Translational Data Science Clusters or teams focused on technologies like genomics, proteomics, spatial technologies, etc.
• Present and externalize team work in conferences, abstracts, and scientific journals.

Qualifications

• Ph.D. in data science, computational biology, or related fields; strong scientific mindset with deep understanding of biology, oncology, and LMW drug development; 10+ years in a data science role in oncology drug development.
• Excellent leadership and management skills with 5+ years of people management experience in data science.
• Experience with QC and data analysis for assays such as bulk RNA-seq, scRNA-seq, 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 contributions to the scientific community via papers or conference presentations.

Skills

• Proficiency in AI/ML approaches, including generative AI in chemistry; computational chemistry knowledge preferred.
• Experience with cell line profiling, perturbation screens (shRNA/CRISPR), high-content imaging, proteomics, or in vivo efficacy analysis.

Education

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

Additional Requirements

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