Position Title: Quantitative Geneticist, Predictive Breeding
Location: South San Francisco, CA
Time Type: Full Time
ResponsibilitiesAs 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.
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.
No recruiters, please.
