---
title: 'Staff Data Scientist, Machine Learning in Epidemiology and Patient Data Products at Valo Health'
canonical: 'https://feeny.ai/job/staff-data-scientist-machine-learning-in-epidemiology-and-patient-data-products-kze6qx4bjp5k'
type: 'job'
last_seen: '2026-09-14'
---

# Staff Data Scientist, Machine Learning in Epidemiology and Patient Data Products at Valo Health

- **Company:** Valo Health
- **Location:** Lexington Massachusetts, United States / San Francisco California, United States
- **Compensation:** $165k–$220k
- **Work type:** remote
- **Posted:** 2026-05-15
- **Last confirmed live:** 2026-09-14
- **Apply:** https://job-boards.greenhouse.io/valohealth/jobs/8550220002

## Job description

## About Us

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.

Our talented team of biologists, chemists and engineers, armed with advanced AI/ML tools, work together to break down traditional R&D silos and accelerate the speed and scale of drug discovery and development.

Valo is committed to hiring diverse talent, prioritizing growth and development, fostering an inclusive environment, and creating opportunities to bring together a group of different experiences, backgrounds, and voices to work together. We embrace new ways of learning, solve complex problems and welcome diverse perspectives that can help us advance patient-centric innovation.

Valo is headquartered in Lexington, MA, with additional offices in New York, NY and Tel Aviv, Israel.  To learn more, visit [www.valohealth.com](http://www.valohealth.com/).

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.

Remote Salary Range $165,000—$190,000 USD

CA Salary Range $175,000—$220,000 USD

Compensation for the role will depend on a number of factors, including a candidate’s qualifications, skills, competencies, and experience. Valo Health currently offers healthcare coverage, annual incentive program, retirement benefits and a broad range of other benefits. Compensation and benefits information is based on Valo Health's good faith estimate as of the date of publication and may be modified in the future.

Please note: At this time, we are only able to consider candidates who currently have permanent US work authorization without the need for immediate or future sponsorship.

## About Valo Health

## Company Overview
- **One-liner**: Valo Health is an AI-driven biotechnology company redefining drug discovery by integrating human causal biology, predictive chemistry, and large-scale patient data to develop novel small molecule therapeutics.
- **Entity Type**: Private
- **Headquarters**: Lexington, Massachusetts, United States
- **Founded**: 2019
- **Founders**: Not publicly available (leadership team includes CEO Brian Alexander, MD, MPH)

## Core Business
- **Primary industry/industries**: Biotechnology, Drug Discovery & Development, Artificial Intelligence in Healthcare
- **Target customers**: B2B (pharmaceutical partners, biotech collaborators) and ultimately B2C (patients with unmet medical needs)
- **Mission or purpose statement**: "To transform drug discovery through AI-enabled human causal biology and predictive chemistry."

## Products & Services
- **Human Causal Biology Platform**: An AI-powered system that analyzes over 17 million de-identified patient records (some spanning 20-30 years with linked biobank samples) to identify patient subtypes, map biological pathways, and pinpoint validated therapeutic targets using causal inference and statistical genetics.
- **Closed-Loop Chemistry Platform**: A tightly coupled modeling and laboratory system that rapidly explores vast chemical spaces, identifies novel lead compounds, and optimizes small molecules for human safety and efficacy, aiming to generate first-in-class drugs.
- **Drug Discovery Pipeline**: A portfolio of internally developed small molecule programs targeting challenging diseases, with a focus on de-risking high failure points and accelerating the path from target identification to clinical candidates.

## Market Standing
- **Valuation/Market Cap**: Not publicly available (private company)
- **Key Metric**: Total Funding — Valo has raised significant venture capital (exact amount not disclosed in available sources; earlier reports indicate over $300M in Series B and other rounds, but recent figures are not confirmed)
- **Notable Investors/Partners**: Board includes Paul Biondi (Executive Chairman), David R. Epstein, and Harsha Ramalingam; strategic partnerships with pharmaceutical and technology organizations (specific names not detailed in provided sources)
- **Growth Signals**: Access to more than 17 million de-identified patient records; named a 2026 "Best Place to Work" by the Boston Business Journal; continued expansion of leadership team with roles in AI, biology, chemistry, and operations

## Competitive Advantages
- **Integrated R&D Engine**: Deep integration of biology, chemistry, and engineering within a single platform, avoiding silos and enabling continuous learning across the drug discovery pipeline.
- **Human-First Approach**: Starts drug discovery with human causal biology (not animal models), using real-world patient data to validate targets before lab experimentation, significantly reducing biological risk.
- **Proprietary Data Assets**: Unique access to a massive, longitudinal patient dataset (17M+ records) linked to biobank samples, providing a rare view of the full disease arc from early symptoms to long-term outcomes.
- **Closed-Loop Learning**: Chemistry models are rapidly refined after each experiment, balancing competing properties (efficacy, safety, pharmacokinetics) to advance the most promising lead series.

## Strategic Focus
- Accelerate discovery of novel small molecules against challenging, traditionally inaccessible targets.
- De-risk clinical development by aligning discovery efforts with desired human clinical profiles from the outset.
- Expand the platform's capabilities in causal AI and predictive chemistry to increase pipeline throughput.
- Continue building a cross-functional team at the intersection of science, technology, and medicine.

## Why Work Here
- **Culture**: Valo emphasizes a culture of curiosity, collaboration, humility, ownership, and impact. They describe themselves as "redefining what's possible" in drug discovery.
- **Work Environment**: Open to remote work for many roles (e.g., Staff Data Scientist positions list "Remote" as an option alongside Lexington, MA). Office locations in Lexington.
- **Benefits**: Medical, Dental, and Vision Insurance; Flexible Time-off; 401K; Commuter Reimbursement; Fitness Reimbursement; Pet Insurance; Corporate engagement opportunities with local non-profit organizations.
- **Recognition**: Named a 2026 "Best Place to Work" by the Boston Business Journal.
- **Engineering & Data Science Focus**: Active hiring for Staff Data Scientists (Graph ML, Machine Learning in Epidemiology), Staff Computational Biologists, and other technical roles. The team works on cutting-edge AI, causal inference, and large-scale data problems.

## Sources
1. [valohealth.com](https://www.valohealth.com/)
2. [valohealth.com/company](https://www.valohealth.com/company)
3. [valohealth.com/approach](https://www.valohealth.com/approach)
4. [valohealth.com/company/careers](https://www.valohealth.com/company/careers)
5. [job-boards.greenhouse.io/valohealth](https://job-boards.greenhouse.io/valohealth)

## Other roles at Valo Health

- [Staff Data Scientist, Computational Biology](https://feeny.ai/job/staff-data-scientist-computational-biology-valo-health-lexington-massachusetts-jxbwhycbajw4) — Lexington Massachusetts, United States