Valo Health is looking to accelerate the creation of life-changing drugs by integrating human-centric data and AI-powered technology, and this role will help design, develop, and apply machine learning models for scientific problems involving clinical and biomedical data.
Requirements
- 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.
Responsibilities
- 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.
Other
- Degree in a quantitative field with 7+ (BS), 5+ (MS), or 3+ (PhD) years of post-degree experience or equivalent
- 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.
