Head of Research, DataLab

Protege

About The Position

Protege’s Head of Research will lead DataLab, the company’s research arm focused on fundamental challenges in data for AI. The role defines the research agenda, builds rigorous experimentation and evaluation systems, and ensures findings inform product direction, customer strategy, and platform capabilities. It combines research leadership with hands-on building: setting a clear roadmap, scaling a high-performing research team, partnering across Product, Engineering, go-to-market (GTM), and company leadership, and turning ambiguous technical questions into practical frameworks, measurable experiments, and actionable recommendations. The role also serves as a customer-facing research partner to GTM across sales, technical discovery, delivery, and partner conversations. The Head of Research brings technical credibility to those engagements and represents Protege’s research perspective externally. DataLab’s work addresses areas such as agentic AI training, large-scale corpus quality control, and evaluation datasets that better reflect real-world use.

Requirements

Has led impactful research initiatives in AI, machine learning, data infrastructure, or applied research environments, with outcomes influencing product or business strategy.
Has built and managed high-performing research teams characterized by autonomy, technical rigor, and fast execution.
Has partnered directly with customers, GTM teams, or external stakeholders in applied technical settings and can credibly communicate the research and business value of complex AI and data work.
Has developed frameworks for evaluating model performance, dataset quality, synthetic data, or large-scale experimentation systems.
Has experience in ambiguous, fast-moving environments with evolving priorities.
Can communicate complex technical findings effectively to technical and non-technical audiences.
Has owned cross-functional initiatives spanning research, engineering, product, and go-to-market teams.
Has translated research into practical systems, tools, or customer-facing impact.
Has experience with modern AI systems, foundation models, LLM evaluation workflows, or privacy-centric data methodologies.
Demonstrates sound judgment, strong prioritization, and comfort making decisions with incomplete information.
Is motivated by building from first principles and solving foundational problems in AI infrastructure.

Responsibilities

Define and lead DataLab’s research strategy, aligning experimentation with company priorities and product direction.
Partner with Product, Engineering, and GTM to identify high-value research opportunities related to AI data quality, evaluation, and marketplace performance.
Design and oversee experiments evaluating dataset quality, model performance, synthetic data workflows, and privacy-preserving methodologies.
Build scalable systems for benchmarking, labeling-quality analysis, and training-data evaluation across multiple AI modalities.
Represent DataLab in sales, technical discovery, delivery, and customer-strategy conversations, translating research into practical guidance for customers and partners.
Translate ambiguous technical questions into research frameworks, measurable hypotheses, and actionable recommendations.
Publish internal research findings that inform product decisions, customer strategy, and platform capabilities.
Lead, manage, and scale a high-performing team of researchers and data scientists, supporting execution, technical excellence, and career development.
Establish operational rigor for experimentation, reproducibility, and research documentation.
Represent Protege in technical conversations with customers, partners, and the broader AI ecosystem.
Stay current with advances in foundation models, evaluation methodologies, data infrastructure, and AI alignment research.
Assess the platform, customer workflows, research priorities, workflows, experimentation infrastructure, and evaluation methodologies; identify opportunities to improve research velocity, rigor, and communication.
Deliver a research roadmap and launch initial experiments; introduce standardized experiment tracking, documentation, and reproducibility processes.
Develop hiring plans and organizational structure, recruit and onboard technical talent, and lead the team as it grows.
Deliver insights that influence product-roadmap decisions or customer outcomes and demonstrate measurable improvements in experimentation speed, evaluation quality, or data performance.