Senior Data Scientist

Valo Health•Lexington, MA

1d•Hybrid

About The Position

At Valo Health, we're building a technology-first, human-centered approach to drug discovery and development. We are seeking a highly motivated and skilled Senior Data Scientist to join our growing team. In this role, you will develop and evaluate novel computational chemistry models and platform data science tooling that will accelerate Valo’s drug discovery mission. You will work closely with data science, machine learning, and medicinal chemistry teams to identify relevant data sources, design modeling experiments, and evaluate model performance to meet the needs of Valo’s drug discovery platform. This will be a hybrid role for either Lexington, MA or San Francisco, CA.

Requirements

A PhD in Computational Chemistry, Computer Science, Data Science or related technical field, with 1-2 years of experience building tools and developing models with demonstrated business impact.
Extensive experience with data science languages and frameworks, such as R and pandas; engineering best practices, such as git and CI/CD pipelines; DevOps tooling like Docker, and Kubernetes; orchestration tooling and ETL pipelines such as Prefect or Airflow
Strong analytical, problem-solving, and communication skills, including experience with Rmarkdown and/or Jupyter Notebooks for communicating results; and the ability to also condense, summarize, and synthesize those results into informative and actionable presentations to less technical audiences.
Experience with building machine learning models, tools, and workflows for drug discovery.
Please note: At this time we are only able to consider candidates who have permanent U.S. work authorization without the need for immediate or future sponsorship.

Responsibilities

Develop solutions for advancing real-world preclinical discovery programs, tackling problems such as ADME modeling, toxicity prediction, and generative chemistry.
Develop and deploy data science tools and workflows to accelerate hit identification, hit-to-lead, and lead optimization efforts.
Design and develop model dashboards to facilitate model performance tracking, discoverability, and provenance.
Implement data science best practices in areas such as rigorous statistical validation, data curation and preparation, model life-cycle management.