Senior Data Scientist, Statistical Genetics

We are a multi-disciplinary team of experts in science, technology, and pharmaceuticals united in our mission to achieve better drugs for patients faster. 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 achieve the widest-ranging impact when we leverage our broad backgrounds and perspectives to accelerate a new frontier in health.

Valo seeks to become the catalyst for the pharmaceutical industry and drive the digital transformation of the industry. Are you ready to join us?

As aSenior Data Scientist on the Statistical Genetics team in the Translation Data Science Group, you will contribute to building a computational platform that will change the way we identify and validate drug targets. You will work in a multidisciplinary environment alongside data scientists, biologists, software engineers, and clinical development experts in building a powerful computational platform for advancing the discovery and development of new medicines.

Using unique and diverse human-centric datasets, we will solve challenging problems at the interface of scientific discovery, methodology, technology, and software. We are looking for data scientists who can match scientific breadth with technical excellence.

In this role, you will be responsible for designing experiments and workflows that leverage cutting edge genetics approaches to discover and develop drug treatments using genetic, multi-omic, and clinical data. Successful candidates will work with a diverse set of scientists, entrepreneurs, and domain experts in ways that cut across traditional industry boundaries.

• Work as a member of a team of world‑class data scientists developing and deploying robust, generalizable solutions to core scientific problems.
• Leverage genetic data to help identify or validate a target for a specific indication and communicate the results to a diverse set of stakeholders.
• Have the opportunity to work with a diverse array of data spanning electronic medical records, genetic sequencing data, 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. Many of the challenges we’re trying to address don’t have known solutions or clear processes to arrive at answers.
• Use your technical knowledge and intuition to articulate and break down large problems into solvable pieces. There are a lot of problems to solve; you’ll need to prioritize which of these are critical‑path today from those that can wait.
• 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.

• BS + 4-8, MS + 3-5 or PhD + 0-3 years experience in statistical genetics, computational sciences (eg, computational biology, molecular biology), or related fields
• Experience in performing and working with results of genetically anchored studies:
• Demonstrated experience with GWAS, (omic)
  QTL, Mendelian randomization, and colocalization
• Familiarity with LoF, PRS, and TWAS are a plus
• Experience with statistical genetics, exploratory data analysis, and data visualization, with a demonstrated business or scientific impact
• Experience in machine learning, probabilistic graphical modeling, and/or causal inference are a plus
• Proficient in Python or R programming and at least basic knowledge of the other
• Familiarity with working in a command line interface
• Familiarity with version control (e.g., git) and code review

• Experience developing software packages
• Experience with working with electronic health record data, personally identifiable information (PII), protected health information (PHI) data, or HIPAA compliance
• Experience researching cardiovascular disease
• Experience in drug discovery and development