Application Guide

How to Apply for Staff Scientist - Causal Inference

at Recursion

🏢 About Recursion

Recursion is a biotechnology company that combines automated experimental biology with artificial intelligence to decode biology and industrialize drug discovery. They're unique for their data-centric approach, using massive biological datasets and computational power to accelerate therapeutic development. Working here offers the chance to apply data science to real-world human impact at the intersection of technology and healthcare.

About This Role

This Staff Scientist role focuses on applying causal inference methods to large-scale healthcare datasets (claims, EHR, genomics) to uncover relationships between clinical factors, genomics, and patient outcomes. You'll produce scientifically rigorous evidence that directly informs clinical development decisions, making this role impactful by bridging statistical analysis with real therapeutic development.

💡 A Day in the Life

A typical day might involve analyzing large healthcare datasets using Python/R to apply causal inference methods, then meeting with computational biology colleagues to interpret findings in biological context. You'd likely prepare evidence summaries for clinical development teams and collaborate on study designs that inform therapeutic program decisions.

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Interview Prep

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🎯 Who Recursion Is Looking For

  • Has 6+ years of hands-on experience applying statistical programming (Python/R) to observational healthcare data like claims or EHR
  • Demonstrates expertise in causal inference methods (propensity scoring, instrumental variables, difference-in-differences) applied to real-world data
  • Has experience developing and validating machine learning predictive models in healthcare contexts
  • Can show evidence of collaborating with cross-functional teams (clinical, biometrics, computational biology) to translate analyses into decisions

📝 Tips for Applying to Recursion

1

Quantify your 6+ years of experience with specific healthcare datasets - mention exact types (e.g., '5 years working with Optum claims data analyzing 2M+ patient records')

2

Include a portfolio link or describe 1-2 causal inference projects with healthcare data, detailing your methodological choices and business impact

3

Highlight any experience with human genomics data specifically, as this is called out in the job description

4

Demonstrate understanding of Recursion's platform by mentioning how your causal inference skills could integrate with their automated biology approach

5

Show collaboration examples with clinical teams - not just technical work but how you've influenced clinical decisions

✉️ What to Emphasize in Your Cover Letter

['Your specific experience with causal inference methods applied to healthcare/clinical data', 'Examples of how your statistical analyses have directly informed clinical development or operational decisions', 'Your ability to communicate complex statistical concepts to non-technical clinical and biology teams', "Why you're drawn to Recursion's data-driven approach to drug discovery specifically"]

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🔍 Research Before Applying

To stand out, make sure you've researched:

  • → Recursion's current therapeutic areas and pipeline (from their website/investor materials)
  • → Their RECursion Operating System and how they generate biological data at scale
  • → Recent publications or presentations by their data science team on causal inference or real-world evidence
  • → Their partnerships with healthcare organizations that might provide the data you'd be working with

💬 Prepare for These Interview Topics

Based on this role, you may be asked about:

1 Walk through a specific causal inference project you've done with healthcare data - what methods you chose and why
2 How would you design a causal analysis to understand the relationship between a genomic marker and clinical outcomes using real-world data?
3 Describe a time you had to explain statistical limitations or assumptions to clinical team members
4 What challenges have you faced with observational healthcare data (missingness, confounding, selection bias) and how did you address them?
5 How would your approach differ when working with claims data versus electronic health records versus genomics data?
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⚠️ Common Mistakes to Avoid

  • Focusing only on predictive modeling without emphasizing causal inference experience
  • Presenting academic/theoretical statistical knowledge without concrete examples of applied work with healthcare data
  • Failing to demonstrate understanding of how this role bridges statistical analysis with clinical development decisions

📅 Application Timeline

This position is open until filled. However, we recommend applying as soon as possible as roles at mission-driven organizations tend to fill quickly.

Typical hiring timeline:

1

Application Review

1-2 weeks

2

Initial Screening

Phone call or written assessment

3

Interviews

1-2 rounds, usually virtual

✓

Offer

Congratulations!

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Good luck with your application to Recursion!

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