Designs and delivers technical demonstrations, proof-of-concepts, architectures, prototypes, and custom applications for life sciences customers. The role addresses drug discovery, clinical analytics, regulatory workflows, omics processing, governance, reproducibility, and validation requirements. It partners with account executives, Customer Success, and Solutions Architects to convert evaluations into production deployments while building reusable technical assets and identifying new customer use cases.

Provide agricultural science expertise to shape predictive grazing systems: translate biology into testable hypotheses, design and run experiments, validate and interpret models, define measurement protocols, identify new datasets and sensors, and partner with ML engineers, product and data teams to improve model performance and product value for farmers.

Work with Applied ML and DataQA on messy, real-world parts and vehicle data. Use SQL and Python to quantify data-quality issues, measure failure rates, decompose problems, produce reproducible analyses, and build lightweight tools or dashboards to inform Product and ML priorities. Paired with mentors, deliver actionable findings that improve parts validation, ordering, automatching, and variant handling.