About Us

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, work 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 www.valohealth.com. 

About the Role

As a Senior Staff Data Scientist on the Statistical Genetics team in the Translation Data Science Group, you will lead teams in applying Valo’s Opal computational platform to 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 leading the application of cutting-edge genetics approaches to discover and develop drug targets using genetic, multi-omic, and clinical data. Successful candidates will lead in matrixed settings, composed of diverse sets of scientists, entrepreneurs, and domain experts in ways that cut across traditional industry boundaries.

What You’ll Do…

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. There are a lot of problems to solve; you’ll need to decide which of these are on the critical path today from those that can wait.

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. Many of the challenges we’re trying to address don’t have known solutions or clear processes to arrive at answers.

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

What You Bring…

BS + 8-10, MS + 5-8 or PhD + 3-5 years of experience in statistical genetics, computational sciences (eg, computational biology, molecular biology), or a related field

Experience leading matrixed teams, exercising creative problem-solving skills, and demonstrated project management skills

Experience in performing and working with results of genetically anchored studies:

Demonstrated experience conducting genome-wide association studies (GWAS) and follow up analyses including computing and using (omic)QTL for Mendelian randomization and colocalization

Demonstrated experience with the computation and application analyses of rare genetic variation (LoF analysis, burden testing, identity-by-descent detection, functional variant annotation)

Experience working with UKBiobank data or similar datasets, integrating genomics, multi-omics, and clinical information

Experience with statistical genetics, exploratory data analysis, and data visualization with a demonstrated business or scientific impact

Experience researching cardiometabolic disease

Proficient in Python or R programming and at least basic knowledge of the other

Familiarity with working in a command line interface

Familiarity with writing / developing code using version control (e.g., git) and collaborative code writing practices such as code review
Experience developing analysis pipelines and/or software packages

You May Also Bring...

Experience in machine learning, probabilistic graphical modeling, and/or causal inference

Experience with working with electronic health record data, personally identifiable information (PII), protected health information (PHI) data, or HIPAA compliance

Experience in drug discovery and development