Valo Health

• lead the development of machine learning (ML) methods and analyses of patient data • perform project-specific hands-on analysis and modeling of high-dimensional longitudinal real-world data • contribute to the design, implementation, and evaluation of innovative machine learning approaches • articulate and break down large problems into solvable pieces • participate in code reviews and provide regular updates on work

• Collaborate with Platform Engineers and Data Scientists to design and implement Valo Health’s Software infrastructure and architecture in multiple environments • Execute Software and Infrastructure maintenance tasks • Create tools and processes to incrementally allow self-service by users to decrease the overall support burden and empower users • Collaborate with data scientists, medicinal chemists and other stakeholders to drive projects in a highly cross-functional environment • Troubleshoot and improve Valo Health’s Software infrastructure • Foster a collaborative, agile, outcome-focused, team culture

• Develop and deploy graph ML solutions to synthesize and extract novel insights from Valo data in the context of a vast corpus of knowledge in medicine, molecular biology, human genetics, and drugs, to drive compute-enabled biological hypothesis generation. • Collaborate closely with a diverse group of data scientists and biologists to enable the contextualization of predicted drug targets within relevant patient subpopulations. • Communicate methodological approaches and key results to internal and external cross-functional stakeholders. • Work with other data scientists, software engineers, and data engineers to continue building and improving Valo’s integrated graph platform to accelerate insights across multiple projects and applications.

• Lead project teams of world-class data scientists developing and applying robust, generalizable solutions to core scientific problems. • Work with a diverse set of stakeholders to develop project plans that support program needs, develop alignment between functional teams and stakeholders, and prioritize. • Work with an array of data types alongside genetic sequencing data, including electronic medical records, multi-omics data, and other data modalities using R or Python in cloud environments. • Be comfortable with scientific uncertainty and embrace curiosity and creative solutions. • Collaborate with drug discovery and clinical development teams to help ensure the relevance and impact of the insights generated by you and your teammates. • Be a dynamic and 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

• Design, develop, test, maintain, and improve an integrated codebase of AI and machine learning tools • Work with data scientists, researchers, product teams, and other domain experts to build solutions for complex data-oriented problems • Develop solutions for advancing real-world preclinical discovery programs, tackling problems such as ADME modeling, toxicity prediction, and generative chemistry.