Staff Data Scientist in Epidemiology and Patient Data Products

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 Staff Data Scientist in Epidemiology and Patient Data Products, you will be a core member of a team of data scientists advancing the discovery and development of new medicines. In this role, you will answer research questions using large real world healthcare databases to inform identification of biological molecules for effective drug development under the guidance of epidemiology program leads. To do so, you will work in partnership with colleagues in machine learning, statistical genetics, and computational biology to develop solutions to challenging computational problems. Successful candidates will work with a diverse set of scientists and domain experts and engage with external partners, in ways that cut across traditional industry boundaries in an innovative startup environment.

What You’ll Do

• As a senior member of our cardiometabolic team, you will lead real world data studies (e.g., electronic medical records) from end-to-end to generate causal evidence for projects in drug discovery and development.
• Translate research questions into observational study designs to generate patient-centric insights from statistical models. Examples include the following:
  • Curation of clinical and non-clinical variables for machine learning models
  • Execution of trajectory modeling techniques using real world data
  • Interpreting machine learning results into patient profiles.
  • Executing post-hoc longitudinal analyses among patient profiles of interest
• Be comfortable with scientific uncertainty and embrace curiosity and creative solutions. Many of the challenges we’re trying to address do not have known solutions or clear processes to arrive at answers.
• Work with a diverse array of data spanning electronic medical records, sequencing, multi-omics data, and other data modalities using R and Python in cloud environments.
• 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

What You Bring

• MPH, MS with 5+ years or PhD in epidemiology or biostatistics with 3+ years of work-related experience applying epidemiological, statistical, and/or machine learning methods to real-world datasets.
• Must have 3+ years of experience developing and executing robust analytical strategies, including cohort and case control study design, using health care databases including electronic health records, administrative claims databases, and/or patient registries.
• Experience leading epidemiologic projects from end-to-end: from translating research questions into observational study designs, contrasting strengths and weaknesses of different study designs and statistical approaches, and generating patient-centric insights from statistical models.
• Extensive experience with causal approaches applied to observational studies, including propensity score methods, bias adjustment, and covariate selection and adjustment.
• Advanced knowledge in biostatistics approaches, including inferential and predictive modeling, and comfortable implementing unsupervised machine learning algorithms