Strategic Leadership: Develop and execute a comprehensive strategy for LLM evaluation, encompassing LLM quality, costs, model performance, model utility (user experience and prompt effectiveness), and responsible AI considerations, in alignment with company-wide efforts and informed by emerging research. Propose and drive cost effective solutions such as small language models (SLMs), and finetuned models and ensemble models. Program Management: Oversee and manage large-scale, cross-functional evaluation programs, ensuring alignment with organizational objectives and timelines. Develop and maintain a robust measurement framework to track and report on LLM performance and user impact. Drive engineering product roadmap to construct automated evaluation pipelines integrated into the product workflow. Applied Science Experience: Utilize data science skills to design experimentation, analyze data, create OKRs, create measurement and metrics, and derive actionable insights to enhance LLM systems. Responsible for influencing product and user experience based on evaluation results. Model and Agent Evaluation: Lead efforts to assess and improve the performance and effectiveness of language models and agents, driving iterative enhancements, including synthetic and manufactured data creation. User Experience Enhancement: Collaborate with User Experience teams to evaluate and optimize user interactions with AI systems, enhancing user satisfaction. Responsible AI (RAI): Implement RAI and DSB principles and guidelines in AI systems, ensuring ethical and unbiased practices in model development and deployment. Contribute to the LLM research body: Form partnerships and lead deep research initiatives in areas of LLM evaluation and user experience optimization that contribute to the scientific body and deepen the product team's understanding and expertise of user mental models of and alignment to LLM-powered experience. Cross-Functional Collaboration: Work with engineering, research, product, and other teams to ensure seamless integration of evaluation processes into the development lifecycle. Stakeholder Engagement: Communicate findings and recommendations to executive leadership, fostering a data-driven culture within the organization. Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ years related experience (e.g., statistics predictive analytics, research) OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 1+ year(s) related experience (e.g., statistics, predictive analytics, research) OR equivalent experience. These requirements include but are not limited to the following specialized security screenings: Ph.D. or Master's in a relevant field (e.g., Data Science, Computer Science, Social science, HCI, etc.) Experience in data science, machine learning, experimentation, and AI, with a track record of delivering impactful results. Experience in program management and leading cross-functional teams. Experience with LLM in finetuning, reinforcement learning, evaluation techniques, implementing RAG techniques, agentic workflows and industry best practices. Development experience in Python Analytical, problem-solving and presentation skills. Understanding of responsible AI principles. Ability to work in a fast-paced and dynamic environment.