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Halter is a venture-backed agtech company transforming grazing-based agriculture through precision livestock management and predictive systems. The company enables farmers and ranchers to optimize herd management without traditional infrastructure (fences, quad bikes, dogs), using data-driven insights and machine learning.
As an Agricultural Scientist, you will be the scientific backbone of Halter's product development, bridging agricultural domain expertise with machine learning and software engineering. You'll work alongside ML engineers, software engineers, and product managers to build the next generation of predictive systems for grazing agriculture.
Key responsibilities include:
- Applying agricultural science to improve existing predictive models and identify new product opportunities
- Translating biological understanding into testable hypotheses that can be validated through data and machine learning
- Designing and executing scientific investigations to enhance model performance and product capability
- Identifying new measurements, datasets, and signals that could improve predictive systems
- Defining measurement protocols for model validation and training
- Evaluating emerging agricultural research for applicability to Halter's products
- Partnering closely with ML engineers throughout the model development lifecycle
- Collaborating with data operations, product, and customer-facing teams to understand field observations and convert them into research questions
- Ensuring predictive models remain biologically realistic and meaningful
- Building relationships with external researchers and subject matter experts
Success in the first several months means developing deep understanding of Halter's predictive systems and data pipelines, building trusted relationships across teams, identifying meaningful opportunities where agricultural science improves product performance, and helping establish effective processes for incorporating scientific thinking into product development.
Required qualifications: MSc or PhD in Agricultural Science, Agronomy, Animal Science, Plant Science, or related discipline; significant experience applying agricultural science to real-world problems; strong understanding of grazing systems and agricultural production; experience designing, conducting, and interpreting scientific investigations; excellent communication skills for explaining complex concepts to multidisciplinary teams; ability to influence technical decisions through expertise.
Strongly preferred: experience with data science, predictive modeling, or machine learning; background in agricultural technology or precision agriculture; strong quantitative and analytical skills; experience with large datasets; collaboration with software engineering or product teams; familiarity with remote sensing, sensor technologies, or digital agriculture.
