Staff Data Scientist, Machine Learning in Epidemiology and Patient Data Products

The role

You will develop machine learning tools for patient data and drive their adoption across teams. You will lead the development of ML methods and analyses of patient data using high-dimensional longitudinal real-world data.

What you will do

• Lead the development of machine learning methods and analyses of patient data with diverse stakeholders.
• Integrate clinical insights into supervised and unsupervised learning approaches and generate patient profiles.
• Perform hands-on analysis and modeling of high-dimensional longitudinal real-world data.
• Contribute to the design, implementation, and evaluation of innovative machine learning approaches for patient data.
• Break down large problems into solvable pieces.
• Champion shared coding standards and participate in code reviews.
• Provide regular updates on work and input into the work of colleagues.

Required qualifications

• MS, MPH, or PhD in health data science, biostatistics, or a related quantitative field.
• 5 years of experience developing and applying ML methods.
• At least 3 years working directly with real-world patient data.
• 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.
• Extensive experience building, maintaining, and operationalizing ML pipelines.
• Broad proficiency across core ML paradigms (supervised, unsupervised, semi-supervised).
• Experience with linear and logistic regression, classification and tree‑based methods, clustering and dimensionality‑reduction techniques, and deep learning architectures.
• Strong grounding in evaluation metrics, hyperparameter tuning, model selection, feature engineering and selection, model explainability, and MLOps best practices.
• Mastery of Python and modern data science tools (scikit-learn, PyTorch, statsmodels, SciPy, MLlib, MLflow).
• Ability to lead and facilitate meetings and work collaboratively on multi-disciplinary project teams.

Nice to have

• Experience in a biopharmaceutical, epidemiological or biostatistical setting
• Experience processing and mining clinical notes
• Hands-on experience with representation learning and transformer-based and other sequence models
• Experience with AI-assisted coding tools (e.g., Claude Code)
• 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

Benefits

• Healthcare coverage
• Annual incentive program
• Retirement benefits
• Broad range of other benefits

Before you apply

• Only able to consider candidates who currently have permanent US work authorization without the need for immediate or future sponsorship.

About the company

Valo Health is a human-centric, AI-enabled biotechnology company working to make new drugs for patients faster. The company’s Opal Computational Platform transforms drug discovery and development through a unique combination of real-world data, AI, human translational models and predictive chemistry.