Overview In this role you translate validated scientific methods into scalable production systems for leadership assessment and talent programs. You will own end-to-end production implementations, shaping software architecture and reliable pipelines used by executives and HR partners. You'll blend scientific rigor with practical engineering to deliver durable, self-serve solutions beyond direct scientist involvement. You influence product and policy through clear trade-offs and measurable impact on leadership development at scale. This is a chance to work at the intersection of science, software, and organizational impact in a high-stakes environment.
Compensation / Benefits- health insurance
- 401(k) matching
- paid time off
- parential leave
- RSUs
- comprehensive benefits
Responsibilities- Own production implementation of scientific systems end-to-end, ensuring reliability and scalability without requiring a scientist on site
- Define architectural and tooling decisions to encode scientific methods into software that is testable, maintainable, and extensible
- Set and model engineering quality standards for scientific code, emphasizing testing, documentation, reproducibility, and peer review in a scientist-led team
- Build and own LLM-powered pipelines for prompt orchestration, retrieval grounding, automated scoring, and evaluation harnesses
- Adapt scientific techniques to product-level needs when established methods fall short, developing new methodological solutions
- Collaborate with Research Scientists to assess feasibility and trade-offs early, contributing actionable engineering judgments
- Create reusable scientific components, services, and templates to enable downstream teams to run methodologies autonomously
- Contribute to quasi-experimental evaluations of people programs, owning analytical implementations and data pipelines
- Mentor scientists on software engineering practices and participate in peer review of experimental designs and code
- Communicate technical choices and trade-offs to product and HR partners, linking design decisions to business outcomes
Key requirements- PhD in a quantitative field
- 5+ years of applied research delivering production-grade, scalable solutions
- Strong Python software engineering skills for production pipelines
- Deep expertise in psychometric measurement/validation, causal inference with observational data, or applied LLM systems
- clear written communication of trade-offs
- mentorship and collaboration with scientists and product partners
- ability to challenge approaches while maintaining scientific rigor
- Python production pipelines
- psychometric measurement and validation
- causal inference (observational and quasi-experimental)
