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Location: Boston/Lexington, MA or Remote USA

About Us

Valo Health is a technology company that is integrating human-centric data and AI-powered technology to accelerate the creation of life-changing drugs for more patients faster. Valo was created with the belief that the drug discovery and development process can and should be faster and less expensive, with a much higher probability of success. We are using models early to fail less often, executing clinical trials to add valuation to the company, and generating fit-for-purpose data to feed back into Valo’s Opal Computational Platform™ as we reinvent drug discovery and development from the ground up. Disease doesn’t wait, so neither can we.

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?

About the Role

As a Senior Epidemiologist and Data Scientist in Epidemiology and Patient Data Products, you will be a core member of a team of epidemiologists, data scientists, and data engineers building a powerful computational platform for advancing the discovery and development of new medicines. In this role, you will answer research questions using large real world healthcare databases to generate hypotheses for drug development under the guidance of epidemiology program leads. To do so, you will work in partnership with colleagues in statistical genetics and machine learning to develop solutions to challenging computational problems. Successful candidates will work with a diverse set of scientists and domain experts, in ways that cut across traditional industry boundaries in an innovative startup environment. 

What You’ll Do…

  • Work as a member of teams of world-class data scientists developing and deploying robust, generalizable solutions to core scientific problems. 
  • 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. 
  • 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 or PhD in epidemiology, biostatistics, applied statistics or related quantitative field 
  • Demonstrated ability to execute robust analytical strategies using health care databases including electronic health records, administrative claims databases, and/or patient registries 
  • Experience with epidemiology research methods including cohort and case control study design, confounding control, and longitudinal methods 
  • Experience with causal approaches applied to observational studies, including propensity score methods, bias adjustment, and covariate selection and adjustment. 
  • Must have experience conducting data manipulation and statistical analysis in Python and/or R programming languages 
  • Confident in your ability to translate statistical model output into patient-centric insights, for example translating machine learning results into patient profiles 
  • Experience developing research proposals and conducting feasibility studies 
  • Comfortable working in ambiguous problem spaces; experience working in a start-up or agile work environment as part of cross-functional project teams 
  • Exceptional time management, ability to prioritize multiple tasks simultaneously, and deliver products on time every time 
  • Advanced knowledge in biostatistics approaches, including inferential modeling, predictive modeling, and implementing unsupervised machine learning algorithms in real world health care databases is a plus 
  • Experience translating machine learning output into meaningful insights for diverse audiences is a plus 
  • Familiarity with or exposure to traditional drug discovery and development processes and approaches is a plus 
  • Familiarity with integrated clinico–omics datasets (including sequencing, genomics, proteomics, etc) is a plus 

More on Valo

Valo Health, Inc (“Valo”) is a technology company built to transform the drug discovery and development process using human-centric data and artificial intelligence-driven computation. As a digitally native company, Valo aims to fully integrate human-centric data across the entire drug development life cycle into a single unified architecture, thereby accelerating the discovery and development of life-changing drugs while simultaneously reducing costs, time, and failure rates. The company’s Opal Computational Platform™ is an integrated set of capabilities designed to transform data into valuable insights that may accelerate discoveries and enable Valo to advance a robust pipeline of programs across cardiovascular metabolic renal, oncology, and neurodegenerative disease. Founded by Flagship Pioneering and headquartered in Boston, MA, Valo also has offices in Lexington, MA, and New York. To learn more, visit www.valohealth.com.

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Confirmed 18 hours ago. Posted 30+ days ago.

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