Staff Data Scientist, Clinical Real World Evidence

Added: 3/25/2021

REF: 8744

Contract: Permanent

Location: Boston, Massachusetts, United States

Senior/Staff Data Scientist, Real World Evidence (Clinical)

Join a team of data scientists building a robust computational platform for advancing the R&D of new medicines. Opportunity to work across traditional industry boundaries in a fast-paced startup environment, with a diverse array of data spanning biology, computational chemistry, imaging, electronic medical records, text notes, clinical trials, and data from our labs.


Day to Day:

  • Work as part of (and lead) teams of data scientists and engineers developing and deploying robust, generalizable solutions to core scientific problems
  • Be comfortable with scientific risk - many of the challenges we’re trying to address don’t have known solutions.
  • Use your technical knowledge and intuition to articulate and break down large problems into solvable pieces in a limited amount of time.
  • Collaborate across clinical and pre-clinical teams to help make sure the models developed by you and your team are appropriate.

  • MS in biological sciences, medically related field, or computer related field
  • Strong understanding of multiple healthcare claims and electronic healthcare records database
  • Practical experience with real world patient data - cleaning, filtering, harmonizing, etc.
  • Experience conducting, analyzing and reporting observational database studies using large observational health related database resources
  • Experience with multiple programming languages, such as R, Python, SQL
  • Experience with statistical analysis and ML pertaining to observational studies, including potential outcome models, counterfactual regression, covariate adjustment, instrumental variables, traditional epidemiological tools, etc.
  • Experience with popular analytical tools such as Pandas, Scikit-learn, Tensorflow, PyTorch, Jupyter, or
  • Experience with modern machine learning techniques pertaining to patient representation learning is a plus
  • Familiarity with or exposure to traditional drug discovery and development processes and approaches is a plus
  • A user-oriented, product mindset and familiarity with agile development practices a plus

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