Senior Applied Scientist (Bing Places)

work on challenging problems that require deep technical expertise and a focus on real‑world impact. work end‑to‑end: from problem formulation and data analysis, through model development and experimentation, to production deployment and live flighting. Formulate complex product and engineering problems as machine learning and AI tasks, and drive them from concept through production. Design, implement, and evaluate ML‑ and LLM‑based models that improve Bing Places quality, relevance, and coverage. Conduct rigorous data analysis to understand system behavior, identify opportunities, and define success metrics. Prototype new modeling approaches and iterate quickly based on offline evaluation and online experimentation. Own experimentation pipelines, including offline validation and large‑scale online A/B flighting. Partner closely with engineers to integrate models into production systems and ensure long‑term reliability and performance. Drive technical direction within your problem space and influence broader modeling and platform decisions. Document and communicate results through technical design reviews, papers, and patent filings. Bachelor's Degree in Computer Science, or Computer Engineering, or related field AND 4+ years related experience OR Master's Degree in Computer Science, or Computer Engineering, or related field AND 3+ years related experience OR Doctorate in Statistics, Econometrics, Computer Science, or Computer Engineering, or related field AND 1+ year(s) related experience OR equivalent experience. Doctorate in Computer Science, or Computer Engineering, or related field AND 3+ years related experience 3+ years of experience applying AI solutions or LLMs to real‑world systems (RAG, ranking, classification, reasoning). Proven experience in distributed training, model optimization, and production ML infrastructure. Hands‑on experience developing and evaluating models on large‑scale, real‑world datasets. Proficiency in Python and experience with modern ML frameworks (e.g., PyTorch, TensorFlow, JAX, or similar). In depth nderstanding of experimentation methodologies, including offline metrics and online A/B testing. Ability to independently scope problems and deliver high‑quality solutions in ambiguous environments. Proven collaboration skills and experience working with engineering and product partners. Ability to clearly communicate technical concepts and trade‑offs to both technical and non‑technical audiences. Comfort operating across the full lifecycle—from research and prototyping to production and live operations.