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Computational Geneticist Jobs (NOW HIRING)

Ohalo

Quantitative Geneticist

South San Francisco, CA · On-site

We are seeking a visionary and hands-on Quantitative Geneticist to be a principal architect of the computational engine that drives our entire crop improvement strategy. This isn't a typical modeling ...

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How much do computational geneticist jobs pay per year?

As of Jun 16, 2026, the average yearly pay for computational geneticist in the United States is $94,262.00, according to ZipRecruiter salary data. Most workers in this role earn between $69,000.00 and $98,500.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Computational Geneticist, and why are they important?

To thrive as a Computational Geneticist, you need a solid background in genetics, bioinformatics, and statistics, typically supported by an advanced degree in genetics, computational biology, or a related field. Proficiency with programming languages (such as Python or R), genomics databases, and specialized software like PLINK or GATK is essential. Strong analytical thinking, problem-solving abilities, and effective collaboration skills help distinguish top performers in this role. These competencies are crucial for interpreting complex genetic data, driving research discoveries, and contributing to advancements in personalized medicine.

What does a computational geneticist do?

A computational geneticist uses computer algorithms, statistical models, and bioinformatics tools to analyze genetic data. Their work often involves studying DNA sequences, identifying genetic variations, and understanding how these variations contribute to traits or diseases. They collaborate with biologists, clinicians, and other scientists to interpret large-scale genomic data and develop insights that can inform medical research, agriculture, or evolutionary studies. The field requires strong skills in programming, mathematics, and genetics.

What are some common challenges faced by computational geneticists in collaborative research projects?

Computational geneticists often collaborate with biologists, clinicians, and data scientists, which can present challenges such as aligning interdisciplinary goals and ensuring clear communication. Differences in technical language and project priorities can require extra effort to establish shared understanding and effective workflows. Additionally, managing and integrating large, complex datasets from diverse sources can be demanding, necessitating robust data management practices and flexibility in adapting analytical approaches. Successful computational geneticists regularly bridge these gaps by fostering strong communication skills and staying adaptable in fast-paced, collaborative environments.
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Computational Geneticist jobs near you

Quantitative Geneticist

Ohalo

South San Francisco, CA • On-site

Full-time

Posted 25 days ago

Job description

Position Title: Quantitative Geneticist, Predictive Breeding
Location: South San Francisco, CA
Time Type: Full Time
The Opportunity
At Ohalo, we are building the future of agriculture with our breakthrough Boosted breeding technology. We are seeking a visionary and hands-on Quantitative Geneticist to be a principal architect of the computational engine that drives our entire crop improvement strategy.
This isn't a typical modeling role. You will be at the nexus of genetics, data science, and engineering, designing the predictive systems that guide our breeding decisions. You will build and deploy everything from genomic selection models to sophisticated simulations that chart the course of our breeding portfolio. If you are driven to solve complex problems and want to see your code and models directly translate into real-world genetic gain, this is a unique opportunity to make a foundational impact.
Responsibilities
As a key member of our technical team, your responsibilities will be organized around three core pillars:
1. Core Predictive Science
  • Genomic Prediction & GWAS: Design, build, and validate the primary statistical models (e.g., GBLUP, ssGBLUP, GWAS) that form the foundation of our predictive capabilities, translating genotype and phenotype data into actionable insights.
  • Breeding Simulation: Evolve our in-house breeding simulation platform to run complex, large-scale scenarios. Your models will answer critical strategic questions about resource allocation, risk management, and the optimal path to achieve our breeding objectives.

2. Strategic Decision Modeling
  • Pipeline Optimization: Move beyond prediction to prescription. Design and implement online optimization models (e.g., using multi-armed bandits, online learning, metaheuristics) to create a self-improving system that dynamically allocates resources and maximizes the rate of genetic improvement.
  • Portfolio Management & Utility: Develop and integrate multi-trait utility functions that align our selection strategy with market needs and product profiles. You will help manage the entire breeding portfolio as a strategic asset.

3. Innovation & Collaboration
  • Accelerate Research with AI: Act as a force multiplier by leveraging modern AI tools across the research lifecycle. This includes using LLMs for hypothesis generation, pioneering the use of genomic foundation models (e.g., Evo2), and using AI-assisted tools to write, debug, and document production-quality code.
  • Drive Cross-Functional Impact: Serve as a critical scientific partner to domain experts (breeders, plant scientists), Machine Learning Engineers (MLEs), and Data Engineers (DEs). Proactively translate breeding objectives into modeling requirements and ensure your solutions are seamlessly integrated into our operational workflows.
  • Uphold Statistical Rigor: Collaborate with fellow quantitative scientists to champion statistical integrity across the organization, from experimental design to model validation and interpretation.
Candidate Profile
  • Education: M.S. or Ph.D. in Quantitative Genetics, Statistical Genetics, Plant Breeding, Biostatistics, Operations Research, or a related computational field.
  • Core Experience: 5+ years of hands-on experience applying quantitative principles in a research or industry setting. A strong portfolio of projects demonstrating the application of predictive modeling and/or simulation is highly desired.
  • Programming Excellence:
    • Expert-level proficiency in Python and its scientific computing stack (e.g., NumPy, SciPy, Pandas, Scikit-learn). Demonstrable experience building modular, testable, and maintainable code is essential.
    • Hands-on experience using generative AI tools (e.g., GitHub Copilot) to accelerate the development of scientific code.
  • Statistical Modeling Expertise:
    • Deep theoretical and practical understanding of mixed models for genetic evaluation (e.g., GBLUP, ssGBLUP).
    • Proven experience with Bayesian statistics, applying methods such as Bayesian GBLUP, hierarchical models, and clustering using MCMC or variational inference.
    • Familiarity with decision theory and online optimization frameworks (e.g., multi-armed bandits, Thompson sampling) for resource allocation.
    • Experience with or interest in applying genomic foundation models (e.g., Evo2, other LLM-like architectures) to learn from large-scale sequence data.
    • Experience with machine learning algorithms (e.g., XGBoost, Ridge Regression) as applied to genomic data.
  • Collaboration & Communication: A proven ability to work effectively in a cross-functional team. You must be able to translate complex technical and scientific concepts for different audiences and work collaboratively to turn models into real-world impact.
  • Genomic Data Acumen: Experience handling and processing large-scale genomic datasets (e.g., SNP arrays, sequencing data) is required.
  • Bonus Points For:
    • Proficiency in R, particularly for reading and translating legacy statistical models (e.g., brms, sommer, ASReml).
    • Experience with workflow management tools (e.g., Nextflow, Snakemake).
    • Familiarity with cloud computing environments (GCP, AWS) and data warehousing technologies (e.g., BigQuery).
    • Knowledge of polyploid genetics and modeling.

The anticipated pay range for this role is $150,000 - $200,000 per year for our San Francisco, CA location, though salary will be based on a variety of factors including, but not limited to, experience, skills, education, and location.
About Ohalo:
Ohalo™ aims to accelerate evolution to unlock nature's potential. Founded in 2019, Ohalo develops novel breeding systems and improved plant varieties that help farmers grow more food with fewer natural resources, increasing the yield, resiliency, and genetic diversity of crops to sustainably feed our population. Ohalo's breakthrough technology, Boosted Breeding™, will usher in a new era of improved productivity to radically transform global agriculture. For more information, visit www.ohalo.com.
Notes: If you previously applied for a job at Ohalo Genetics, we encourage you to restate your interest in the position by submitting your application.
Ohalo is an Equal Opportunity / Affirmative Action employer committed to diversity in the workplace. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, age, national origin, disability, protected veteran status, gender identity or any other factor protected by applicable federal, state or local laws. Ohalo is also committed to working with and providing reasonable accommodations to individuals with disabilities. Please let your recruiter know if you need an accommodation at any point during the interview process.
No visa sponsorship is available for this position at this time.
This organization participates in E-Verify. Posters linked here.
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