Job Description

Senior Computational Biologist - Early Discovery Oncology

New York, New York; Salt Lake City, Utah

Your Work Will Change Lives. Including Your Own.

As a computational biologist within Recursion's early discovery team, you will be at the forefront of reimagining drug discovery from first principles. Your mission is to integrate Recursion's massive datasets—phenomics, transcriptomics, genomics, proteomics—to nominate novel targets, validate disease biology, and define the precision oncology strategies to advance our most promising therapeutic targets into full-fledged drug discovery programs.

This is a highly collaborative role: you will partner with biologists, chemists, data scientists, platform and data engineers, and translational experts to develop and scale methods for target discovery and validation. You will also partner with computational scientists from across the organization to develop novel approaches to agentic target packages, early translational de-risking, and mechanism of action identification. The ideal candidate has strong stakeholder management, technical communication skills, and 3+ years biotech or pharma experience in early drug discovery.

We are hiring for two roles on a new target ID team, one domain expert in immunology and inflammatory diseases and one in precision oncology (this role).

In this role, you will:

• Drive target discovery: Evaluate the molecular evidence for target nomination and validation, leveraging Recursion's platform to identify novel vulnerabilities in precision oncology disease biology
• Pilot novel methods for target discovery, validation, and early patient population hypotheses to send programs into nomination cascades
• Define internal criteria for target data packages and early patient population strategy
• Connect your innovative analysis methods to platform and engineering teams so that we solve the current project and accelerate future ones
• Collaborate cross-functionally: Partner with biologists, medicinal chemists, translational, and portfolio leads to build compelling data packages that drive program milestones and internal investment decisions.
• Present data analysis to decision makers and stakeholders in a clear and compelling way that drives toward getting medicines to patients.

The team you'll join:

Our group is a bold, agile, diverse collective of computational drug discovery scientists deeply focused on the singular goal of bringing new therapeutics into the clinic at an accelerated pace. Our expertise spans precision oncology, I&I (immunology and inflammation), and neuroscience and we focus on advancing novel, targeted therapies in these disease areas. We partner closely with other functions to design and execute impactful and decisional data analyses for new targets and drug programs and are responsible for data strategy across the portfolio. We prioritize career development, fluency at the intersection of biology and technology, and a strong network of technical feedback and support.

The experience you'll need:

• PhD in a relevant field (computational biology, bioinformatics, systems biology, cancer biology, etc.) with a very strong computational focus and 3+ years of experience in biotech or pharma industry OR MS in a relevant field and 5+ years of experience in biotech or pharma industry solving fundamental problems in early drug discovery
• Domain expertise in precision oncology and target id
• Strong understanding of patient genetics and druggability of disease relevant pathways
• Experience applying computational methods (including probabilistic, statistical, and/or machine learning techniques) to analyze and integrate complex biological and/or human clinical data in a high level programming language such as Python or R
• Exceptional data visualization skills
• Excellent cross-functional communication skills, including an ability to explain complex scientific concepts to a variety of audiences using a combination of plots, documents, and presentations

Nice to have:

• Experience building a target discovery engine using public and proprietary datasets
• Experience designing high-throughput screens with CRISPR or small molecule perturbations to validate therapeutic hypotheses
• Previous exposure to efficient drug program advancement from proof of concept to IND

Working location & compensation:

This is an office-based, hybrid role in our Salt Lake City, UT or New York, NY offices. Employees are expected to work in the office at least 50% of the time.

At Recursion, we believe that every employee should be compensated fairly. Based on the skills, experience, and qualifications needed for this role, the estimated annual base salary range is:

• New York City: $166,980 to $220,660
• Salt Lake City: $151,800 to $200,600

In addition to base salary, this role is eligible for an annual bonus, equity compensation, and a comprehensive benefits package.

The values we hope you share:

• We act boldly with integrity. We are unconstrained in our thinking, take calculated risks, and push boundaries, but never at the expense of ethics, science, or trust.
• We care deeply and engage directly. Caring means holding a deep sense of responsibility and respect - showing up, speaking honestly, and taking action.
• We learn actively and adapt rapidly. Progress comes from doing. We experiment, test, and refine, embracing iteration over perfection.
• We move with urgency because patients are waiting. Speed isn't about rushing but about moving the needle every day.
• We take ownership and accountability. Through ownership and accountability, we enable trust and autonomy—leaders take accountability for decisive action, and teams own outcomes together.
• We are One Recursion. True cross-functional collaboration is about trust, clarity, humility, and impact. Through sharing, we can be greater than the sum of our individual capabilities.

Our values underpin the employee experience at Recursion. They are the character and personality of the company demonstrated through how we communicate, support one another, spend our time, make decisions, and celebrate collectively.

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