Recursion PharmaceuticalsData Scientist

Updated Aug 4, 2026 · Reviewed by the Dataford team

Recursion Pharmaceuticals Data Scientist interview questions & guide 2026

Every question Recursion Pharmaceuticals interviewers actually ask, the frameworks that win the room, and the language hiring managers respond to.

4 rounds · ≈ 3-5 weeks

Recruiter Screening

Hiring Manager Discussion

Technical Evaluations

Panel Loop

What is a Data Scientist at Recursion Pharmaceuticals?

As a Data Scientist at Recursion Pharmaceuticals, you sit at the exciting intersection of advanced computational science, artificial intelligence, and industrialized drug discovery. Your work directly drives the refinement of the Recursion OS, helping decode complex biological and chemical datasets to accelerate the discovery of life-changing medicines. You will build and scale high-impact systems, turning massive arrays of experimental and proprietary data into structured insights that fuel automated scientific reasoning and machine learning frameworks.

The impact of this role is profound and foundational to the company's mission. You will partner closely with cross-functional teams including machine learning engineers, wet-lab biologists, and medicinal chemists to design context retrieval strategies, manage biological knowledge graphs, and develop agent-driven AI workflows. Whether you are optimizing vector representations for large-scale language model deployments or engineering data pipelines that ingest and harmonize public and private biomedical resources, your technical contributions enable automated systems to reason about disease biology with unprecedented scale and precision.

Expect an environment that demands rigorous scientific thinking alongside production-grade engineering excellence. Recursion Pharmaceuticals operates at a scale where traditional bioinformatics methods must be reimagined to leverage high-performance computing architectures and cutting-edge GenAI ecosystems. Success in this position requires a rare blend of deep domain knowledge in biology or chemistry, sophisticated data manipulation skills, and the pragmatism needed to translate complex prototypes into robust, production-grade workflows.

Common Interview Questions

The following questions are representative of those asked in real interview loops for this position. They illustrate the core patterns and technical challenges you will encounter, rather than serving as a rigid memorization checklist.

SQL and Data Manipulation

Recursion values your ability to extract, clean, and structure data efficiently from diverse relational and unstructured sources. Expect tests of your query writing and data transformation capabilities.

How would you use SQL window functions to track consecutive experimental assay failures over time for a specific molecular target?
Write a query to calculate rolling 30-day averages of high-throughput screening metrics across multiple distinct laboratory batches.

How do you handle missing or malformed biological identifier records when joining massive genomic datasets using SQL?
Given a table of compound testing results, how would you rank compounds within each assay category using advanced grouping and ranking functions?
Optimize a slow-running query that aggregates complex cell-painting feature vectors across millions of experimental wells.

Experimentation and A/B Testing

Rigorous experimental design is critical when evaluating new algorithmic models or high-throughput assay protocols. Interviewers will test your understanding of controlled testing environments.

How would you design an A/B testing framework to evaluate two different retrieval strategies in a biological knowledge retrieval system?
What are the most common experimentation pitfalls you encounter when running concurrent offline model evaluations?
How do you determine sample size and power when dealing with noisy, low-throughput biological assay validation experiments?
Explain how you would handle network effects or data contamination when splitting datasets for model-driven experimentation.
Walk through a scenario where an experiment shows a surprising outcome due to selection bias and how you would diagnose it.

Product Metrics and Diagnosis

You must be able to define success metrics for complex technical systems and diagnose sudden shifts in performance.

How would you design a core set of product metric design indicators to measure the reasoning efficiency and accuracy of a biological AI agent?
Imagine a critical metric tracking successful context retrieval drops by fifteen percent overnight. How would you approach this metric drop diagnosis step by step?
What key performance indicators would you establish to monitor the operational health and data freshness of an automated biomedical pipeline?
How do you balance latency, computational cost, and retrieval accuracy when defining optimization targets for automated scientific reasoning tools?
When a newly deployed feature vector index leads to unexpected downstream model degradation, what telemetry metrics do you inspect first?

Statistics and Probability

Statistical rigor underpins all data-driven decision making and model evaluation at the company.

How do you establish statistical significance when comparing the efficacy distributions of compounds tested in replicated high-throughput assays?
Explain the difference between false discovery rate control and family-wise error rate correction in high-throughput biological screening.
How would you apply Bayesian methods to update prior beliefs about a drug target as new experimental assay data streams in?
When facing severe class imbalance in phenotypic screening data, what statistical metrics and resampling techniques do you rely on?
How do you validate that your underlying data distributions meet the assumptions of parametric statistical tests used in your analysis pipelines?

Behavioral and Leadership

Recursion looks for candidates who embody core values such as acting boldly with integrity and moving with urgency because patients are waiting.

Tell me about a time you had to make a difficult technical trade-off under tight timelines. How did you balance speed and scientific rigor?
Describe a project where you collaborated with non-technical stakeholders, such as wet-lab biologists, to translate a complex scientific question into a data architecture.
How do you handle disagreements within a cross-functional team regarding architectural direction or feature prioritization?
Share an example of a time an experiment or model failed completely. How did you diagnose the failure, learn from it, and pivot your approach?

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03 · Question bank

The questions most likely to come up

Sorted by relevance to this company

Rolling Averages Across BatchesMedium

Calculate batch-level rolling 30-day screening averages using joins, daily aggregation, and window frames.

Data AnalysissqlAggregations

Evaluate Predictive Power of a ModelMedium

Assess whether a model has real predictive power using validation performance, calibration, and threshold behavior.

Cross-ValidationMAERMSE

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Getting Ready for Your Interviews

Preparation for this loop requires balancing deep technical foundations with practical software engineering and scientific reasoning. You should review core statistical concepts, sharpen your querying skills, and be ready to discuss your past projects in exhaustive detail.

Role-related knowledge – You must demonstrate mastery over your core technical domain, whether that involves bioinformatics toolkits, cheminformatics libraries, or advanced machine learning frameworks. Interviewers will probe the architectural decisions behind your past projects, expecting you to defend your choice of data structures, retrieval strategies, and modeling approaches.

Problem-solving ability – You will face open-ended technical challenges and ambiguous scientific scenarios that test your structured thinking. Interviewers want to see how you break down complex problems, formulate hypotheses, test assumptions methodically, and adapt when initial results are inconclusive.

Leadership and collaboration – Because this role sits at the nexus of biology, chemistry, and software engineering, cross-functional communication is vital. You must be able to articulate complex technical trade-oids clearly to audiences with diverse scientific backgrounds while championing ownership and accountability.

Culture alignment – Recursion places immense value on working with urgency, acting with integrity, and learning rapidly through active experimentation. Your behavioral and technical discussions should reflect a mindset that embraces iteration over perfection and maintains an unwavering focus on the ultimate mission of helping patients.

Interview Process Overview

The interview process at Recursion Pharmaceuticals is thorough, structured, and designed to evaluate both your technical capabilities and your alignment with the company's scientific mission. The journey typically begins with an initial recruiter screening followed by a detailed discussion with the hiring manager to explore your background, technical depth, and past project experiences. Candidates who advance then face rigorous technical evaluations, which often include timed coding assessments or work sample assignments focusing on data manipulation, statistics, and machine learning.

The later stages culminate in a virtual or onsite panel loop where you will meet with multiple members of the data science, engineering, and scientific teams. These sessions dive deep into your architectural vision, problem-solving methodologies, and behavioral alignment through targeted discussions on values and experimental design. The process is demanding and comprehensive, reflecting the high-stakes nature of TechBio innovation and the importance of cross-functional team cohesion.

06 · The loop

The interview process, end to end

≈ 3-5 weeks · 4 rounds

Recruiter Screening

Initial contact with a recruiter to assess candidate fit and background.

Hiring Manager Discussion

Detailed conversation with the hiring manager to explore technical depth and past project experiences.

Technical Evaluations

Rigorous assessments including timed coding tests or work samples focusing on data manipulation, statistics, and machine learning.

Panel Loop

Virtual or onsite sessions with multiple team members to discuss architectural vision and problem-solving methodologies.

The visual timeline above outlines the progression from initial recruiter touchpoints through technical screening, work samples, and comprehensive panel loops. You should pace your preparation to endure multiple rounds of technical scrutiny without burning out early. Expect varied formats ranging from live coding and architecture deep dives to cultural and behavioral interviews with cross-functional partners.

Deep Dive into Evaluation Areas

SQL and Data Manipulation

Your ability to manipulate, clean, and query data is foundational to building reliable pipelines and extracting insights from massive biomedical repositories. Interviewers evaluate your fluency in writing clean, performant queries and your ability to handle complex relational structures without manual intervention.

Be ready to go over: – SQL window functions – Utilizing ranking, aggregation, and analytical sliding frames for time-series and experimental batch data. – Pipeline optimization – Structuring ingestion workflows and relational transformations to handle high-throughput output.

– Data hygiene – Handling sparse records, missing biological identifiers, and schema drift across disparate databases. – Advanced concepts (less common) – Query execution plan tuning, columnar database optimizations, and complex graph-to-relational data translation strategies.

Example questions or scenarios:

"Write a query utilizing window functions to identify the top performing compound batches across sequential screening runs."
"How would you restructure a sluggish SQL join operation that merges multi-terabyte genomic datasets?"

A/B Testing and Experimentation

Designing rigorous experiments is essential when validating new algorithms, retrieval mechanisms, or assay workflows. You must show that you understand how to isolate variables and measure true impact without falling prey to common biases.

Be ready to go over: – Experimental design – Structuring randomized trials and offline evaluations for complex tech stacks. – Experimentation pitfalls – Identifying and mitigating selection bias, novelty effects, and data leakage. – Power calculations – Determining adequate sample sizes when dealing with high-variance biological readouts. – Advanced concepts (less common) – Quasi-experimentation methods, multi-armed bandit allocation strategies, and cluster-randomized trial designs.

Example questions or scenarios:

"How would you design an experiment to test whether a new vector retrieval model improves biological reasoning accuracy?"
"Walk through a scenario where an A/B test shows a statistically significant lift that turns out to be a false positive due to data contamination."

Product Metrics and Diagnosis

Data scientists must be able to translate high-level product and scientific goals into measurable metrics and quickly diagnose operational anomalies when those metrics fluctuate.

Be ready to go over: – Product metric design – Establishing robust key performance indicators for agentic AI systems and data retrieval pipelines. – Metric drop diagnosis – Methodically isolating root causes when a critical telemetry or accuracy metric degrades.

Monitoring telemetry – Setting up real-time dashboards and automated alerts for data freshness and pipeline health.
Advanced concepts (less common) – Multi-objective optimization metrics, trade-off analysis between latency and retrieval precision, and synthetic metric benchmarking.

Example questions or scenarios:

"Your system experiences a sudden drop in context retrieval relevance. Outline your diagnostic workflow."
"What metrics would you prioritize when evaluating the integration of external knowledge graphs into an automated reasoning workflow?"

Statistics and Probability

Statistical reasoning ensures that conclusions drawn from noisy biological assays and complex model outputs are valid and reproducible.

Be ready to go over: – Statistical significance – Applying appropriate hypothesis testing frameworks to high-throughput data.

Error rate control – Managing false discovery rates and multiple testing corrections across thousands of features.
Probabilistic modeling – Utilizing Bayesian updating and likelihood estimations for target identification.
Advanced concepts (less common) – Non-parametric permutation tests, survival analysis for compound toxicity screening, and hierarchical linear modeling.

Example questions or scenarios:

"How do you correct for multiple hypothesis testing when screening millions of chemical compounds against a biological target?"
"Explain how you would apply Bayesian statistics to update assay confidence scores as new replicated data arrives."

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08 · Topic breakdown

What they actually test for

Topic distribution

All topics

PythonRAG (Retrieval-Augmented Generation)Agentic AI SystemsData Pipelines (Automated ingestion/transformation/refresh)Machine Learning

Key Responsibilities

As a Data Scientist at Recursion Pharmaceuticals, your day-to-day work centers on architecting and maintaining the data infrastructure and reasoning systems that power AI-driven drug discovery. You will spend a significant portion of your time designing automated pipelines that ingest, transform, and refresh critical internal and external biological resources without manual intervention. This involves working extensively with public databases, structured and unstructured knowledge repositories, and vector-based retrieval architectures.

You will collaborate fluidly across organizational boundaries, partnering with wet-lab biologists, medicinal chemists, and core platform engineers to ensure that computational models accurately reflect the complexities of disease biology. Your responsibilities include piloting tool use for large language models, building robust interfaces for autonomous data querying, and translating high-potential prototypes into scalable production workflows. You will also present technical trade-offs—such as graph versus vector representations—to leadership and cross-functional stakeholders, ensuring technical execution aligns with broader product and scientific visions.

Role Requirements & Qualifications

To be competitive for this role, you must bring a powerful combination of rigorous academic training, technical mastery, and collaborative acumen. Recursion looks for professionals who can bridge the gap between theoretical data science and production-grade software engineering within a life sciences context.

Must-have technical skills – Advanced proficiency in Python and software engineering best practices including CI/CD, Git version control, and modular design. Strong expertise in utilizing public biological databases, standard bioinformatics or cheminformatics toolkits, and designing automated data pipelines.
Must-have experience – Advanced degree in a relevant computational or biological field with substantial industry experience focusing on biological data representation, retrieval systems, or applied machine learning.
Must-have soft skills – Exceptional cross-functional communication abilities, with a proven track record of explaining complex architectural decisions to both scientific domain experts and technical stakeholders.
Nice-to-have qualifications – Hands-on experience with modern GenAI frameworks, RAG systems, vector databases, agentic AI frameworks, and fine-tuning foundation models on scientific corpora.

Frequently Asked Questions

Q: How difficult is the interview loop, and how much preparation time should I plan for? The interview process is rigorous and multi-staged, reflecting the high technical bar at Recursion Pharmaceuticals. Candidates typically spend several weeks reviewing core statistical concepts, practicing SQL and coding assessments, and structuring examples from their past projects.

Q: What differentiates successful candidates from those who do not pass? Successful candidates combine deep technical competence with a pragmatic understanding of biological data complexity. They excel at communicating their architectural decisions clearly, demonstrating both scientific curiosity and strong software engineering discipline.

Q: What is the company culture like for data scientists? The culture emphasizes bold thinking, active experimentation, and radical cross-functional collaboration. Teams move with urgency because patients are waiting, fostering an environment where accountability and direct engagement are paramount.

Q: What is the typical timeline from initial screen to offer? The timeline can vary depending on team scheduling and the thoroughness of the evaluation stages, often spanning several weeks from the initial recruiter chat through the final panel loop and work sample review.

Q: Are the roles remote, hybrid, or office-based? This position is office-based and hybrid, requiring employees to work from either the Salt Lake City, UT or New York City, NY offices for at least fifty percent of the time.

Other General Tips

Master the fundamentals: Brush up on standard theories, statistical tests, and SQL window functions so you can answer foundational questions without hesitation.
Structure your system design: When discussing architectures like knowledge graphs versus vector databases, clearly articulate the trade-offs regarding latency, scalability, and biological fidelity.
Embrace the scientific context: Show genuine curiosity about biology and drug discovery; demonstrating that you understand the domain problems makes your technical contributions much more impactful.
Communicate trade-offs openly: Interviewers want to see how you make decisions under constraints. Always explain why you chose a specific approach, what alternatives you considered, and how you evaluated success.
Align with company values: Weave stories of ownership, urgency, and cross-functional collaboration into your behavioral answers to resonate with the core tenets of Recursion Pharmaceuticals.

Tip

When discussing technical projects, always highlight how your work impacted downstream scientific users or automated reasoning systems.

Summary & Next Steps

Stepping into the Data Scientist role at Recursion Pharmaceuticals offers a rare opportunity to pioneer AI-driven breakthroughs in drug discovery. By uniting massive biological datasets with advanced reasoning systems, your work will directly empower automated discovery platforms and change lives. Success in this demanding loop hinges on mastering core technical areas such as SQL window functions, A/B testing, experimental design, and robust data pipeline architecture.

To maximize your readiness, focus your preparation on structural problem-solving, statistical rigor, and clear communication of complex architectural trade-offs. Candidates can explore additional interview insights, practice questions, and preparation resources on Dataford to refine their strategy and approach every round with confidence. Embrace the challenge with curiosity and urgency, knowing that focused preparation will materially elevate your performance.

14 · Compensation

What this role pays

2 reports

USUSD

Estimated total compLow confidence · 2 data points

$0k-$0k

Median $118k / year

Base salary · 100%Stock (RSU) · 0%Cash bonus · 0%

25thEntry / smaller markets

$56k

50thTypical offer

$118k

90thTop performers / major metros

$180k

Breakdown by component

Base salary

100% of total

$56k$180k

$118k

median

Stock (RSU)

0% of total

$0$0

median

Cash bonus

0% of total

$0$0

median

Aggregated from 2 self-reported salaries via Glassdoor. Estimates only. Verify against your offer.

The compensation data reflects the estimated annual base salary range for this position, typically spanning from $200,600 to $238,400, alongside annual bonuses, equity compensation, and comprehensive benefits. Candidates should interpret this range as reflective of senior-level technical impact and specialized domain expertise. Reviewing these figures helps you align your expectations and negotiate effectively based on your specific background and experience level.

15 · More at this company

Other roles at Recursion Pharmaceuticals

Machine Learning Engineer Research Scientist Research Analyst Software Engineer Product Manager

17 · FAQ

Recursion Pharmaceuticals Data Scientist interview FAQ

Answered from real candidate and compensation data

What is the interview process like for Recursion Pharmaceuticals Data Scientist?

The loop typically includes recruiter screening, a hiring manager discussion, technical evaluations, and a panel loop. Technical evaluations can include timed coding tests or work samples focused on data manipulation, statistics, and machine learning. The panel loop involves virtual or onsite sessions with multiple team members to discuss architectural vision and problem-solving approaches.

How hard is it to get an offer for Recursion Pharmaceuticals Data Scientist?

In candidate-reported experience, the most common difficulty level is average. Across 12 reported interviews, the offer rate reported is 0%. This means you should expect a competitive process and prepare for more than just one type of assessment.

What technical topics does Recursion Pharmaceuticals test for a Data Scientist?

Expect coverage across Python and machine learning, with recurring themes like RAG (Retrieval-Augmented Generation), agentic AI systems, and knowledge graphs. Data pipelines for automated ingestion, transformation, and refresh are also common, alongside statistics and experimentation concepts. SQL and data manipulation are tested as well, including query writing, window functions, and handling missing or malformed identifiers.

What kinds of SQL and data manipulation questions come up for Recursion Pharmaceuticals Data Scientist?

You may be asked about using SQL window functions to track consecutive failures over time or calculating rolling averages across batches. Other common patterns include ranking compounds within assay categories using grouping and ranking functions, optimizing slow-running aggregation queries, and joining large datasets while handling missing or malformed biological identifiers. Preparation should focus on query structure, correctness, and performance for large-scale data.

What machine learning and GenAI question types should I prioritize for Recursion Pharmaceuticals Data Scientist?

You should be ready for questions tied to machine learning for complex problems, including RAG, agentic AI systems, and systems grounded in domain truth. Agent or retrieval workflows are a recurring theme, and your preparation should connect model behavior to measurement and operational signals. Public sample questions include “Implement Decision Tree From Scratch” and “Machine Learning for Complex Problems.”

What compensation can I expect for Recursion Pharmaceuticals Data Scientist?

Candidate and job-posting reports show base pay ranging up to $56k and total compensation up to $180k. Pay varies by level and location, so the relevant number for you depends on which tier you are interviewing for. If you are comparing offers, use total compensation rather than base alone.

Recursion Pharmaceuticals Data Scientist interview questions & guide 2026

What is a Data Scientist at Recursion Pharmaceuticals?

Common Interview Questions

SQL and Data Manipulation

Access the full Recursion Pharmaceuticals Data Scientist prep plan

The questions most likely to come up

Getting Ready for Your Interviews

Interview Process Overview

The interview process, end to end

Deep Dive into Evaluation Areas

SQL and Data Manipulation

Access the full Recursion Pharmaceuticals Data Scientist prep plan

What they actually test for

Key Responsibilities

Role Requirements & Qualifications

Frequently Asked Questions

Other General Tips

Tip

Summary & Next Steps

What this role pays

Other roles at Recursion Pharmaceuticals

Other Data Scientist guides

Recursion Pharmaceuticals Data Scientist interview FAQ