Director of Research, DataLab
Lead Protege's DataLab research organization, defining strategy for AI training data quality, evaluation systems, and marketplace optimization while managing researchers and partnering with Product, Engineering, and GTM.
About the job
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
Skills
Ai Research, Machine Learning, Llm Evaluation, Dataset Quality Evaluation, Synthetic Data, Experimentation Frameworks, Research Strategy, Team Leadership, Foundation Models, Privacy-Preserving Methodologies
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