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Data Scientist – Machine Learning
- Boston, Massachusetts, United States
- Boston, Massachusetts, United States
About
Data Scientist (Machine Learning) leveraging a multi-modal cancer dataset to develop machine learning models integrating molecular and clinical data to advance cancer biology understanding and improve patient outcomes. Job Responsibilities
Design, build, and iteratively refine novel ML models using modern architectures and classical statistical methods for translational oncology. Develop multi-modal modeling integrating RNA-seq with mutations, copy number alterations, fusions, protein markers, and clinical metadata. Translate model outputs into improvements on the Caris clinical diagnostic platform to support better treatment predictions. Publish in peer-reviewed journals and present at scientific conferences and internal forums. Support biopharma collaborations with analytical expertise, custom analyses, and external stakeholder communication. Stay current with ML research, tools, architectures, and development paradigms. Required Qualifications
Ph.D. in CS, Computational Biology, Applied Mathematics, or related; or M.S. with 3+ years relevant experience. Familiarity with representation learning, attention-based architectures, foundation models, and self-supervised learning. Statistical modeling knowledge for clinical data: GLMs, survival analysis, Bayesian methods. Experience building and applying novel ML models beyond off-the-shelf. Proficiency in Python; PyTorch or TensorFlow; scikit-learn, pandas, NumPy, SciPy. Strong written and verbal communication. Linux and Git. Microsoft Office Suite (Word, Excel, Outlook) and business internet tools. Preferred Qualifications
Cancer/molecular biology; large-scale genomics datasets. Peer-reviewed publications. Computer vision for digital pathology. NLP of EHR/real-world data. Cloud model deployment and MLOps. Annual Hiring Range
$125,000-$150,000. Conditions of Employment
Pre-employment: criminal background check, drug screening, credit check (some roles), and reference verification.
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Languages
- English
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