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.
About the Role
As a Staff Data Scientist, Machine Learning, you will be a core member of a team of data scientists and engineers building a powerful computational platform for advancing the research and development of new medicines. As part of the Translational Platform Engineering team, you will help design, develop, and apply machine learning (ML) models, methods, and pipelines for scientific problems involving clinical and biomedical data. Successful candidates will work with a diverse set of data scientists, biological scientists, epidemiologists, and software engineers in ways that cut across traditional industry boundaries.
What You’ll Do…
- Propose, design, and develop ML approaches on high dimensional electronic health records and omics data leveraging Valo’s proprietary platform (data assets and data science packages).
- Design, develop, and support ML pipelines, workbenches, and dashboards to enable users to solve scientific problems.
- Develop well-designed, tested, and documented software packages.
- Collaborate with cross-functional teams and stakeholders to derive user requirements, maintain alignment, and ensure the relevance and impact of models, analyses, and pipelines.
- Be an active team member in code, design, and analysis review.
What You Bring...
- Degree in a quantitative field with 7+ (BS), 5+ (MS), or 3+ (PhD) years of post-degree experience or equivalent
- Broad experience in ML including supervised learning, unsupervised learning, dimensionality reduction, clustering, metrics, model selection, feature selection, and explainability (3+ years required).
- Demonstrated experience with ML on electronic health records (2+ years required).
- Proficient in Python (5+ years required) and experience with ML and data science packages (e.g., scikit-learn, statsmodels, scipy, MLlib).
- Experience with MLops methodology such as workflow orchestration (e.g., Airflow, Prefect), experiment tracking (e.g., MLflow), containerization (e.g., Docker), and reproducible research (3+ years required).
- Experience with collaborative software development using source control management (e.g., git, unit testing, code review, CI/CD) (3+ years required).
- Experience with large-scale data analytics engines (e.g., Spark or Dask) and working in cloud environments (e.g., AWS) (2+ years required).
- Experience with statistical methods such as hypothesis testing, longitudinal modeling, and time to event analysis.
- Strong work ethic with a bias for execution and an ability to manage multiple priorities, ambiguity, and tight timelines. Ability to work effectively in teams or independently.
- Experience with omics data is a plus.
- Familiarity with the drug discovery and development process is a plus.