About the Role...

As a Staff Data Scientist, Machine Learning in Epidemiology and Patient Data Products, you will be a core member on a team of data scientists building a powerful computational platform for advancing the discovery and development of new medicines. In this role, you will develop machine learning tools for patient data and drive their adoption across teams, under the guidance of epidemiology and biology program leads. Successful candidates will work with a diverse group of scientists and domain experts, in ways that cut across traditional industry boundaries in an innovative startup environment.

What You’ll Do…

Your primary areas of responsibility will be:

• As a senior member of our team, you will lead the development of machine learning (ML) methods and analyses of patient data with diverse stakeholders. For example, integrate clinical insights into supervised and unsupervised learning approaches and generate patient profiles.
• Perform project-specific hands-on analysis and modeling of high-dimensional longitudinal real-world data, spanning electronic medical records (EHRs), clinical notes, sequencing data, and multi-omics, using modern data science tools in cloud environments.
• Contribute to the design, implementation, and evaluation of innovative machine learning approaches for patient data to provide novel clinical insights.
• Be comfortable with scientific uncertainty and embrace curiosity and creative solutions. Many of the challenges we tackle don’t have known solutions or established pathways.
• Use your technical knowledge and intuition to articulate and break down large problems into solvable pieces. There are a lot of problems to solve; you’ll need to prioritize which of these are critical-path today from those that can wait.
• Be a dynamic and active team member, championing shared coding standards, participating in code reviews, and providing regular updates on your work and input into the work of your colleagues.

What You Bring…

• MS, MPH, or PhD in health data science, biostatistics, or a related quantitative field, with 5 years of experience developing and applying ML methods, including at least 3 years working directly with real-world patient data. Experience in a biopharmaceutical, epidemiological or biostatistical setting is a plus.
• Extensive experience developing and implementing machine learning solutions in healthcare databases, including EHRs, administrative claims, and patient registries. Familiarity with U.S. and global medical coding ontologies and data models (ICD, ATC, LOINC, SNOMED, CPT, HCPCS, OMOP, etc.). Confident working with highly sparse and high-dimensional data. Experience processing and mining clinical notes is a plus.
• Extensive experience building, maintaining, and operationalizing ML pipelines, and translating model outputs into meaningful insights for diverse audiences.
• Broad proficiency across core ML paradigms (e.g., supervised, unsupervised, semi-supervised) and experience with linear and logistic regression, classification and tree‑based methods, clustering and dimensionality‑reduction techniques, and deep learning architectures. Hands-on experience with representation learning and transformer-based and other sequence models is a plus.
• Strong grounding in key components of the ML development lifecycle, including evaluation metrics, hyperparameter tuning, model selection, feature engineering and selection, model explainability, and MLOps best practices.
• Mastery of Python and modern data science tools (e.g., scikit-learn, PyTorch, statsmodels, SciPy, MLlib, MLflow). Experience with AI-assisted coding tools (e.g., Claude Code) is a plus.
• Comfortable working in ambiguous problem spaces; experience working in a start-up or agile work environment as part of cross-functional project teams.
• Ability to lead and facilitate meetings and work collaboratively on multi-disciplinary project teams.
• Exceptional time management, ability to prioritize multiple tasks simultaneously, and deliver products on time every time.
• Enthusiastic about documentation–ensuring that all analyses are clear and reproducible with thorough documentation of key assumptions and decision points.

You May Also Bring…

• Advanced knowledge of biostatistics approaches, including inferential and predictive modeling. Experience in causal approaches for observational studies, including propensity score methods, bias adjustment, and covariate selection and adjustment.
• Familiarity with or exposure to traditional drug discovery and development processes and approaches.