AI First Design Researcher

About the role

Build. Scale. Sustain. PALO IT is a global technology consultancy that crafts tech as a force for good. We design, develop and scale digital and sustainable products and services to unlock value across the triple bottom line: people, planet, profit. We do the right thing, and we do it right. We're proud to be a World Economic Forum New Champion, and a B Corp-certified company. We are small enough to care locally, big enough to deliver globally (5 continents, 18 offices, +650 experts from +50 nationalities) We are robust and resilient (100% independent and 0 debt) We are entrepreneurs and passionate experts: We invest in what we believe genuinely and work as a collective intelligence We are positive, courageous, caring, doers and committed to excellence About Gen-e2 While the market is still largely AI-augmenting delivery, we have reinvented the SDLC to be AI First. Our approach is a game-changer in productivity and quality, with a strong collaboration between AI generative and our best talents: We now generate 95% of the entire product - code, documentation, infrastructure as code, and even design - with GitHub Copilot The quality consistently exceeds the output of our best traditional engineering teams A product repository houses all product artefacts, giving AI full project context for higher-quality generation A library of rules and prompts defines coding standards, design principles, and security guidelines With Gen-e2, we deliver end-to-end products 2-3× faster than traditional approaches, while raising the bar for engineering excellence. Your Role As a Research Ops & Insights Enablement , you will establish, scale, and govern the Research Ops practice to enable continuous, reliable, and decision-oriented research across the organization. You will build the operational backbone that allows mixed-method research to happen with speed, rigor, and trust, while integrating AI as an accelerator for analysis and synthesis. 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

Requirements

• 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
• Soft Skills
• Strong critical thinking
• Advanced synthesis and pattern recognition
• Excellent stakeholder management
• Strong analytical judgment
• Ability to bring clarity in complex and ambiguous environments
• AI-Native Engineering (Core Expectation)
• 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)
• More About PALO IT
• We're eager to adapt to change, learn from our experiences and move to meet our planet's ur

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