Associate Director, Computational Biology

Associate Director, Computational Biology

Lexington, Massachusetts, United States

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, works 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

As Associate Director, Computational Biology, you will lead a diverse team of technical experts developing and applying cutting‑edge quantitative approaches to drive therapeutic discovery. Our innovative platform leverages advanced computational techniques, starting with patient data, to bring better medicines to patients faster. You and your team will apply and extend our platform to uncover causal biology driving disease and treatment outcomes, focusing on multi‑omics data in advanced human‑centric in vitro models and inpatient samples, as well as cutting‑edge network analysis and graph ML approaches.

A successful candidate combines deep biological knowledge and understanding with a breadth of technical skills in high‑dimensional data analysis, as well as curiosity, willingness to learn, and proven interest and ability in mentoring both early‑career data scientists as well as senior technical experts.

• Manage and mentor early‑career data scientists and senior technical experts, fostering technical growth and cross‑functional communication while building a culture of rigor, accountability, innovation, and collaboration.
• Support external partnerships by aligning deliverables to partner expectations, ensuring project delivery with high scientific standards, and managing direct and indirect stakeholder interactions.
• Design, develop, and implement multi-omic, graph ML, and other high‑dimensional analyses of relevant biological data to support identification of therapeutic targets, biomarkers, and disease mechanisms.
• Collaborate cross‑functionally with other data science teams (e.g., Statistical Genetics, Real‑World Data / Real‑World Evidence), as well as Discovery Biology and therapeutics experts to integrate diverse inputs into cohesive execution plans driving differentiated insights.
• Work with Platform Engineering teams to identify and implement improvements to our platform.
• Participate in planning discussions and provide inputs around prioritization and resource allocation.
• Stay abreast of emerging trends in relevant domains and apply new insights to strengthen program strategy and execution; work with data science and enterprise leadership to set direction for innovation.
• Be comfortable with scientific uncertainty and embrace curiosity and creative solutions. Many of the challenges we’re trying to address don’t have known solutions or clear processes to arrive at answers.
• Be an active team member
  , championing and adopting shared coding standards, participating in code review, and providing regular updates of your work and input into the work of your colleagues.

• PhD in Computational Biology, Systems Biology, Bioinformatics, or related quantitative field in the biological sciences.
• Team leadership experience managing technical teams and fostering collaborative team environments.
• Prior industry experience, including leadership of cross‑functional projects or programs.
• Demonstrated expertise in analysis of high‑dimensional biological data (e.g., genomic, proteomic, epigenomic, etc.)…