Team Leader in Data Science, Disease Area X
$160,300–$297,700 year
HybridCambridge, Massachusetts, United States
Job Summary
Lead data science strategy and execution for hypothesis-driven discovery programs, driving multi-omics analytics across genomics, transcriptomics, proteomics, single-cell, spatial, imaging, and clinical modalities to support target and biomarker portfolios. Translate scientific questions into computational strategies by selecting fit-for-purpose statistical, machine learning, AI, and bioinformatics approaches, while operationalizing responsible generative and agentic AI tools in drug discovery workflows. Contribute hands-on technical work in scientific software development, data engineering, and workflow automation to enable scalable use of proprietary and public datasets. Prioritize resources across multiple projects, balance strategic impact with delivery timelines, and lead a multidisciplinary team of data scientists through coaching and development. Partner cross-functionally with wet-lab scientists, translational researchers, platform teams, and senior stakeholders to shape experimental design and accelerate decision-making. Promote FAIR data practices, reproducible research, and high-quality documentation standards across the team.
Required Qualifications
- Advanced degree (PhD preferred) in Data Science, Computational Biology, Bioinformatics, Computational Science, Molecular Biology, Genetics, Biochemistry, Engineering, or a related quantitative or life sciences discipline
- 6+ years of relevant experience applying computational biology, bioinformatics, AI/ML, statistics, or data science to drug discovery, translational research, biotechnology, pharmaceutical R&D, technology, or academic research
- Experience leading or managing internal data scientists, computational biologists, bioinformaticians, or machine learning scientists in a matrix management environment as well as external collaborators
- Demonstrated ability to lead complex, hypothesis-driven scientific analyses using biological, multi-omics, or translational datasets, including RNA-seq, single-cell RNA-seq, proteomics, genomics, spatial biology, and/or imaging
- Strong practical experience with scientific software development, reproducible analysis, workflow orchestration and collaborative development practices; experience in Python and/or R, with familiarity in tools such as GitHub, HuggingFace, workflow managers, Jupyter notebooks, containers
- Deep experience with cloud-based or enterprise-scale compute platforms, high-performance computing
- Significant experience influencing and collaborating across diverse scientific teams, including wet-lab biology, translational research, engineering, and computational functions
- Familiarity with modern AI/ML methods and their application to biological or biomedical data (i.e. generative, agentic AI)
- Experience acquiring, curating, and engineering proprietary and public datasets while maintaining appropriate data governance, privacy, and security standards
- Demonstrated ability to shape scientific strategy cross-functionally, influence senior stakeholders, and translate analytical results into portfolio-relevant decisions
- Track record of scientific impact through publications, conference presentations, internal decision support, or portfolio contributions
- Strong communication, interpersonal, ethical judgment, resilience, and self-awareness skills
Desired Qualifications
- Artificial Intelligence (AI)
- Biostatistics
- Business Value Creation
- Change Management
- Curious Mindset
- Data Governance
- Data Literacy
- Data Quality
- Data Science
- Data Visualization
- Deep Learning
- Graph Algorithms
- Learning Agility
- Machine Learning (ML)
- Machine Learning Algorithms
- Python (Programming Language)
- Stakeholder Engagement
- Statistical Analysis
- Time Series Analysis
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