Applied Scientist II

Investigate search and retrieval quality problems using data, offline evaluation, and statistical analysis. Develop and test methods for query understanding and sparse, dense, or multimodal retrieval, including large language model techniques where appropriate. Build and analyze evaluations and experiments; explain results, limitations, and recommendations to partners. Collaborate with engineering and product teams to integrate validated approaches and assess quality, latency, and efficiency. Document findings and refine solutions based on evaluation results and product needs. Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 2+ 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 1+ year(s) related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field OR equivalent experience. These requirements include but are not limited to the following specialized security screenings: Experience with information retrieval or search relevance, including query understanding, sparse or dense retrieval, or multimodal retrieval. Experience with Python and a deep-learning framework such as PyTorch. Experience designing offline evaluations or analyzing online experiments. Experience partnering with engineers to bring machine-learning methods into product systems. Familiarity with large language models or agentic search and methods for evaluating grounded results. Experience using quantitative methods to investigate a technical or product problem and communicate findings. Experience programming to implement or analyze models or data.