Senior Computational Biologist – Target ID

Recursion•Salt Lake, NY

11d•Hybrid

About The Position

Your work will change lives. Including your own. The Impact You’ll Make As a computational biology specialist on our Target ID team, you will be at the forefront of transitioning Recursion to its next era of drug discovery. You will serve as a critical biological anchor for a highly technical data science team building the next generation of our target discovery pipelines. The team’s mission is to identify novel therapeutic targets at scale across the genome for hundreds of indications. You will have access to all of Recursion's data layers—including massive internal maps, functional genomics, and rich patient data (transcriptomics, genetics, and Real-World Data/EHR)—and will be tasked with proposing, piloting, and deploying new methods to integrate these datasets. Crucially, you will leverage your biological subject matter expertise to orient and focus your data scientist and software engineer peers towards reliable and correct usage of biological data and feasible candidate programs. As we build out semi-automated and agentic tools (e.g., multi-agent LLM systems for portfolio oversight and target assessment), your deep real-world biology experience will guide the development of these tools, ensuring they are grounded in biological reality and translate to meaningful portfolio and patient impact. The ideal candidate is a computational biology specialist with high fluency in data science tech stacks. You know how to build and evaluate predictive models and build agentic pipelines, and your differentiator is your deep understanding of patient-relevant datasets, disease biology, and target discovery. In this role, you will: The Team You’ll Join Our group is a bold, agile, diverse collective of data scientists and computational biologists driving Recursion’s early portfolio strategy. We are focused on aggressively expanding our early-stage pipeline with highly validated, novel therapeutic targets. To achieve this, we prioritize defining specific patient populations and establishing clear, data-backed translational paths from day one of every new program. Because this team is dominated by experts in traditional data science, AI/ML, and software engineering, your role is absolutely critical. You will provide the essential biological experience and subject matter expertise to improve and accelerate the work of your peers. Essential attributes for this role include a bold, execution-first attitude, high technical fluency to collaborate seamlessly on data-science-heavy codebases, and a passion for deploying rigorous science to develop life-changing medicines.

Requirements

PhD in Computational Biology, Bioinformatics, Human Genetics, Systems Biology, or a closely related field with 3+ years of experience in the biotech or pharma industry OR MS in a relevant field and 5+ years of industry experience focusing on target ID or early drug discovery.
Biological SME: Deep, real-world experience in target discovery, functional genomics, and understanding the druggability of disease-relevant pathways.
Data Science Fluency: Highly proficient in Python and/or R, with a strong ability to seamlessly integrate into a data-science-heavy tech stack. Comfortable with modern software engineering practices, cloud computing, and machine learning concepts.
Patient Data Expertise: Extensive experience working with "discovery-related" human data, particularly transcriptomics (bulk/single-cell), patient genetics/GWAS, and clinical/real-world data (e.g., EHR).
Analytical Rigor: Experience applying advanced computational and statistical methods to establish biological causality (e.g., survival analysis, causal inference modeling).
Excellent cross-functional communication skills, with a proven track record of upskilling peers in biological concepts and guiding technical teams toward translatable biological outcomes.

Nice To Haves

Experience working with, prompting, or integrating Large Language Models (LLMs) and autonomous agentic workflows into scientific research.
Familiarity with building or utilizing Knowledge Graphs for target identification.
Experience working in the field of oncology or immunology.

Responsibilities

Discover & Evaluate: Propose and validate novel targets using deep integration of patient data (transcriptomics, population genetics, EHR, etc) and Recursion's internal multi-omic data layers.
Guide & Automate: Collaborate closely with data scientists to build, refine, and guide semi-automated and agentic target discovery tools. You will ensure these tools are biologically sound and can scale to evaluate hypotheses across many indications spanning a wide range of therapeutic areas.
Triangulate: Use advanced statistical methods (e.g., causal inference, survival modeling) to establish confident target-to-patient connections and define specific addressable patient populations for early pipeline programs.
Bridge the Gap: Translate complex platform findings into disease-relevant applications, bridging the gap between high-dimensional data science output and actionable drug discovery insights.
Present & Influence: Communicate complex biological rationale and data analyses to decision-makers and cross-functional stakeholders, driving data-backed "go/no-go" decisions in a two-stage target approval process.

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