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, and imaging modalities to support target and biomarker portfolios. Translate scientific questions into computational strategies, operationalizing responsible use of generative and agentic AI tools while contributing hands-on technical work in scientific software development and scalable analytical pipelines. Partner cross-functionally with wet-lab scientists and platform teams to shape experimental design, prioritize resources across multiple projects, and communicate findings through internal presentations and external forums. Lead and coach direct reports to foster a high-performing, scientifically rigorous team environment while promoting FAIR data practices and reproducible research standards.
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
- 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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