Applied Research Lead

Lead, grow, and mentor a multidisciplinary team of researchers and applied scientists. Set the research and applied-science strategy for agentic AI in Excel. Drive scientific direction for agent architecture, model and tool selection, prompts, skills, planning, and human-agent collaboration. Define how agent quality is measured through representative benchmarks, graders, regression suites, and multi-turn evaluations. Establish release-readiness criteria for new models and agent capabilities. Lead evaluation and integration of frontier foundation models, balancing quality, latency, reliability, safety, capacity, and cost. Diagnose agent failures using trajectory analysis, experimentation, and customer-representative scenarios. Translate research prototypes into production-ready capabilities in partnership with engineering, product management, and design. Build effective operating mechanisms for model comparisons, quality reviews, research sharing, and technical decision-making. Develop strong partnerships with internal model teams, research organizations, and external AI laboratories. Communicate technical findings and recommendations to senior leadership. Support external research engagement through publications, patents, workshops, and conferences. Recruit, onboard, and develop research talent while establishing durable technical leadership across the team. Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 8+ 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 6+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 5+ years related experience (e.g., statistics, predictive analytics, research) OR equivalent experience. 3+ years of people management experience. These requirements include but are not limited to the following specialized security screenings: Advanced degree in Computer Science, AI, Machine Learning, HCI, Software Engineering, or equivalent experience. Experience leading research and applied science teams focused on LLMs, AI agents, and production AI systems. Deep knowledge in LLM evaluation, benchmarking, experimentation, failure analysis, and model quality. Demonstrated technical judgment across agent architectures, prompting, tooling, reliability, safety, latency, and cost. Track record of translating research innovations into large-scale commercial products. Experience leading cross-functional initiatives spanning research, engineering, product, and design, while influencing decisions through data and experimentation. Experience building and evaluating AI agents, model optimization, evaluation infrastructure, and enterprise productivity applications, with recognized technical contributions and strong executive communication skills.

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