Director of Research, DataLab

Responsibilities

• Define and lead the research strategy for Protege's DataLab, aligning experimentation with company priorities and product direction
• Partner closely with Product, Engineering, and GTM teams to identify high-value research opportunities tied to AI data quality, evaluation, and marketplace performance
• Design and oversee experiments that evaluate 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
• Serve as a customer-facing research partner to GTM, representing DataLab in sales, technical discovery, delivery, and customer strategy conversations
• Translate ambiguous technical questions into clear research frameworks, measurable hypotheses, and actionable recommendations
• Publish internal research findings that directly influence product decisions, customer strategy, and platform capabilities
• Lead, manage, and scale a high-performing team of researchers and data scientists, driving execution, technical excellence, and career development
• Establish operational rigor around experimentation, reproducibility, and research documentation
• Represent Protege externally through technical conversations with customers, partners, and the broader AI ecosystem
• Stay at the forefront of advancements in foundation models, evaluation methodologies, data infrastructure, and AI alignment research

Requirements

• Led impactful research initiatives in AI, machine learning, data infrastructure, or applied research environments where outcomes influenced product or business strategy
• Built and managed high-performing research teams that operated with autonomy, technical rigor, and fast execution cycles
• Experience partnering directly with customers, GTM teams, or external stakeholders in applied technical settings
• Developed frameworks for evaluating model performance, dataset quality, synthetic data, or large-scale experimentation systems
• Experience operating in ambiguous, fast-moving environments where priorities evolved quickly
• Strong ability to communicate complex technical findings to both technical and non-technical audiences
• Demonstrated ownership of cross-functional initiatives spanning research, engineering, product, and go-to-market teams
• Track record of translating research into practical systems, tools, or customer-facing impact
• Experience working with modern AI systems, foundation models, LLM evaluation workflows, or privacy-centric data methodologies
• High judgment, strong prioritization skills, and comfort making decisions with incomplete information