About the Role

We are looking for an Intern to join the Data Science & Engineering (DSE) team, which is developing EntityRisk’s core methodology and software. Our proprietary platform estimates the individual benefits of treatment—through advanced modeling techniques and integrated clinical trial, genomic, and real-world data. As part of the DSE team, your work will be foundational to our product offerings, some of which include:

  • Analysis of all current and potential surrogate measures and their connections to critical endpoints of value to patients and payers
  • Customized individual and subpopulation-level survival and treatment duration curves
  • Scenario planning for pipeline and inline assets
  • Event and cash flow forecasting and analytics for efficacy-linked instruments
  • Value modeling

The Intern position is a role for candidates pursuing their bachelor’s, master’s, or PhD degree in a quantitative field such as statistics, economics, mathematics, finance, or computer science. A strong candidate will have a quantitative educational background as well as relevant experience in the field and will be eligible for a promotion to the Data Analyst, Data Scientist, or Data Engineer position, depending on interest and skills.

Interns are exposed to each stage of a data science workflow, including: building robust data pipelines; developing fit-for-purpose statistical and machine learning algorithms; translating predictions into meaningful quantities for decision making; and communicating results. They contribute to internal software libraries and help clients solve specific problems as part of consulting projects. 

Ideal candidates are collaborative and intellectually curious with a desire to expand their skills and knowledge. Successful candidates will have good written and verbal communication skills in addition to strong technical skills. Candidates do not need to have experience with all the methods and programming languages that we use but should be excited to learn about them.

  • Assist in building data pipelines that take in a range of real-world (e.g., medical claims, electronic health records) or clinical trial data assets and create output in the form of analytic datasets using relevant tools (e.g., SQL, Python, etc.)
  • Maintain and contribute to a database of randomized clinical trial treatment effects spanning multiple salient disease areas
  • Identify high-leverage opportunities associated with new and upcoming therapies through review of pharmaceutical and clinical trial pipelines
  • Assist with implementation of algorithms to (i) predict health outcomes and estimate causal treatment effects, (ii) measure treatment value and calculate value-based prices, and (iii) assess financial risk using relevant programming languages (Python, R)
  • Apply statistical and machine learning methods for prediction of health outcomes and estimation of heterogeneous treatment effects
  • Help implement Bayesian models to combine real-world and clinical trial data using software such as Stan, JAGS, or PyMC
  • Contribute to internal software libraries by implementing new modeling features, creating unit tests, writing documentation, and enforcing style conventions
  • Follow software best practices including version control (Git), code review, and continuous integration
  • Perform literature reviews related to therapeutic and disease areas of interest, regulatory and health authority guidelines, and innovative pricing
  • Present and communicate results to team members and clients

  • Pursuing a BA/BS or MS in a quantitative field
  • Fluency in at least one of R, Python, or SQL
  • Some experience writing technical documents (reports, manuscripts, presentations)

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