Application Guide
How to Apply for Senior Scientific Data Engineer, Data Platform
at Recursion
🏢 About Recursion
Recursion is a biotechnology company pioneering industrial-scale drug discovery through its unique approach combining automated experimental biology with advanced data science. Unlike traditional biotechs, they treat biology as an information problem, using their massive proprietary dataset of cellular microscopy images and chemical libraries to accelerate therapeutic development. Working here means contributing to groundbreaking science that could transform how medicines are discovered.
About This Role
As Senior Scientific Data Engineer for the Data Platform, you'll architect and scale systems that enable scientists to query and analyze Recursion's diverse biological datasets (chemistry libraries, cellular images, assay results). This role bridges data engineering and drug discovery science, requiring you to build infrastructure that makes heterogeneous scientific data relatable and queryable to drive future discoveries. Your work directly impacts the company's core mission of industrializing drug discovery through data.
💡 A Day in the Life
You might start by reviewing platform performance metrics and addressing any issues with data pipelines handling cellular image data, then collaborate with scientists to understand new requirements for querying chemical libraries, followed by designing schema changes to improve relatability between assay results and other datasets. Throughout the day, you'd mentor junior engineers on best practices for scientific data engineering while contributing to platform scaling decisions.
🚀 Application Tools
🎯 Who Recursion Is Looking For
- Has 5+ years building cloud-based data platforms with specific experience in Recursion's tech stack: Python, dbt, Prefect, BigQuery, Datastream, FiveTran, PostgreSQL, GCS, Kubernetes, CI/CD, and Infrastructure as Code
- Holds a degree in drug-discovery related science (Chemistry, Biology, etc.) enabling them to understand the scientific context of the data they're engineering
- Thrives in ambiguous, collaborative environments where they must mentor others while solving complex technical challenges with scientific datasets
- Can articulate how they've previously made heterogeneous datasets queryable and relatable, particularly in scientific or research contexts
📝 Tips for Applying to Recursion
Explicitly connect your data engineering experience to scientific or research contexts - highlight projects where you worked with biological, chemical, or experimental data
Demonstrate your understanding of Recursion's unique approach by mentioning how you'd handle their specific data types (cellular microscopy images, chemistry libraries, assay results)
Showcase mentorship experience beyond just technical leadership - provide examples of coaching, sponsoring, or knowledge sharing across teams
Prepare specific examples of working with ambiguity in technical projects, especially where scientific requirements evolved during development
Tailor your resume to highlight exact matches with their listed tech stack (Python, dbt, Prefect, BigQuery, etc.) rather than generic data engineering skills
✉️ What to Emphasize in Your Cover Letter
['Your experience making scientific or research data queryable and relatable - provide a concrete example with measurable impact', 'How your scientific background (Chemistry/Biology degree) informs your data engineering decisions and enables collaboration with scientists', 'Specific experience with their mentioned technologies, particularly in building scalable data platforms rather than just using individual tools', "Your approach to mentoring and knowledge sharing in technical teams, with examples of elevating others' capabilities"]
Generate Cover Letter →🔍 Research Before Applying
To stand out, make sure you've researched:
- → Recursion's OS (Operational System) and how they industrialize drug discovery - understand their unique data generation pipeline
- → Their published research or case studies showing how they use data science in drug discovery (check their website and scientific publications)
- → Their specific therapeutic areas and pipeline to understand what biological problems you'd be supporting
- → Their engineering blog or tech talks to understand their current data platform architecture and challenges
💬 Prepare for These Interview Topics
Based on this role, you may be asked about:
⚠️ Common Mistakes to Avoid
- Presenting as purely a technical data engineer without demonstrating understanding of or interest in the scientific context
- Generic data engineering experience without specific examples of building platforms (vs. using existing ones)
- Failing to show mentorship or collaborative experience - this role explicitly requires coaching and knowledge sharing
📅 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:
Application Review
1-2 weeks
Initial Screening
Phone call or written assessment
Interviews
1-2 rounds, usually virtual
Offer
Congratulations!