Ohalo · 3 months ago
Quantitative Geneticist, Predictive Breeding
South San Francisco
Full-time
Onsite
Mid, Senior Level
$125K/yr - $150K/yr
2+ years expOhalo is focused on revolutionizing agriculture with its innovative Boosted breeding technology. They are looking for a visionary Quantitative Geneticist to architect computational systems that enhance crop improvement through predictive modeling and simulations.
AgricultureBiotechnologyGenetics
No H1B
Responsibilities
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
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
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
Qualification
Required
M.S. or Ph.D. in Quantitative Genetics, Statistical Genetics, Plant Breeding, Biostatistics, Operations Research, or a related computational field
2-5+ years of hands-on experience applying quantitative principles in a research or industry setting
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
Hands-on experience using generative AI tools (e.g., GitHub Copilot) to accelerate the development of scientific code
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
A proven ability to work effectively in a cross-functional team
Experience handling and processing large-scale genomic datasets (e.g., SNP arrays, sequencing data)
Preferred
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
Company
Ohalo
To support a growing population efficiently, we need to shrink the land, water and energy footprint of our crops.
51-200 employees
Funding
Current Stage
Growth StageTotal Funding
$40M2024-01-25Series Unknown· $40M
Leadership Team
David Friedberg
Chief Executive Officer
Judson Ward
Founder and Chief Technology Officer
Recent News
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2025-02-07
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2025-02-07
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