Data Scientist – Machine Learning
Caris Life Sciences
9 hours ago
On-site
Boston, MA
IT
Position Summary
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/applying novel ML models beyond off-the-shelf.
- Proficiency in Python; PyTorch or TensorFlow; scikit-learn, pandas, NumPy, SciPy.
- Strong written/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.
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/applying novel ML models beyond off-the-shelf.
- Proficiency in Python; PyTorch or TensorFlow; scikit-learn, pandas, NumPy, SciPy.
- Strong written/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.