Quantitative Geneticist, Predictive Breeding

Overview

Position Title: Quantitative Geneticist, Predictive Breeding

Location: San Francisco, CA

Time Type: Full Time

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.

• 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.
• Strategic Decision Modeling
• Pipeline Optimization:
  Move beyond prediction to prescription. Design and implement online optimization models (e.g., using multi-armed bandits, online learning, meta heuristics) 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.
• 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.

• Education: M.S. or Ph.D. in Quantitative Genetics, Statistical Genetics, Plant Breeding, Biostatistics, Operations Research, or a related computational field.
• Core
  
  Experience:
  
  2-5+ 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 (Num Py, Sci Py, Pandas, Scikit-learn). Demonstrable experience building modular, testable, and maintainable code is essential.
  • Hands-on experience using generative AI tools (e.g., Git Hub 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…