Data Scientist, RWD

Added: 3/4/2021

REF: 8921

Contract: Permanent

Location: Boston, Massachusetts, United States

Data Scientist, Real World Data

Building a best-in-class platform focused on developing and advancing new medicines. As part of a wider data team you will be working with top class data scientists and engineers. You will develop solutions covering the gambit of diverse data including medical records, clinical trail data, imaging etc. developed from our own labs. We're excited to have you join our journey. As a venture back start up and having just received our series B we're on a mission to unite human and machine intelligence and change the way the drug development industry works.  

Day to Day:

  • Work as part of (and lead) teams of world-class 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. Time is limited, you’ll need to prioritize which problems are critical-path today from those that can wait.
  • Collaborate with clinical and pre-clinical groups to help ensure the relevance and impact of the models developed by you and your team
  • 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


Requirements: 

  • MS or PhD in a medically-related field (medicine, public health, health economics, biostatistics) with a quantitative focus or in in a quantitative field (computer science, statistics) with experience working on observational medical data
  • Practical experience cleaning, filtering, harmonizing real world patient data is a plus
  • Experience across multiple programming languages such as Python, R, SQL, Julia.
  • Experience with popular analytical tools such as Pandas, Scikit-learn, Tensorflow, PyTorch, Jupyter, or spark.ml
  • Extensive experience with causal statistical analysis and machine learning pertaining to observational studies, including potential outcome models, counterfactual regression, covariate adjustment, instrumental variables, traditional epidemiological tools, etc.
  • 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
  • Experience working with large scale databases of observational patient data
  • Knowledge of computer science concepts pertaining to algorithmic complexity and distributed/parallel computing is a plus

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