Define and scale the Research Ops model, including governance, standards, workflows, and ownership
Establish ethical standards, consent practices, research governance, and participant data management
Build operational systems for continuous discovery and ongoing research programs
Enable mixed-method research through structured triangulation of qualitative and quantitative signals
Design and optimize research workflows from intake and prioritization to repository, synthesis, and activation
Build and maintain the research tooling ecosystem, including platforms such as Dovetail, Maze, Looker, and Hotjar
Integrate AI-assisted analysis and synthesis, leveraging GPT and automation to accelerate pattern detection, summarization, and insight distribution
Create scalable systems for knowledge management, taxonomy, tagging, repository quality, and evidence retrieval
Define and track OKRs and KPIs tied to uncertainty reduction, decision quality, research adoption, and operational efficiency
Partner with Product, Design, Data, and Engineering to embed research into roadmap and decision-making cycles
Improve the speed, consistency, and usability of insights across teams
Act as a strategic enabler for research maturity, helping teams move from ad hoc studies to a trusted continuous-learning system
Proven experience in Research Ops, UX Research Operations, Insights Operations, or Research Program Management
Strong command of advanced mixed methods, including qualitative and quantitative triangulation
Solid understanding of applied statistics and decision-oriented research interpretation
Hands-on experience with research repositories and insight platforms such as Dovetail and Maze
Familiarity with analytics and behavior tools such as Looker and Hotjar
Experience using AI tools for research analysis, synthesis, and knowledge scaling
Strong understanding of research governance, ethics, consent, and operational quality
Ability to design scalable processes that support continuous discovery and democratized research access
Use Generative AI coding tools (e.g., GitHub Copilot, Cursor) as a first-class engineering assistant for:
Code scaffolding and refactoring
Code generation and optimisation
Test-cases and documentation generation
Build applications through AI-driven development practices, including:
AI-assisted debugging and troubleshooting
Intelligent code completion and pattern recognition
Automated documentation generation
Apply prompt engineering best practices for reliable, repeatable engineering outcomes.
Validate GenAI output (determinism checks, guardrails, fallback logic)
For more on our team culture and benefits, check out our careers page.
