Applied Scientist II (Bing Places)

## About the role - 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 ## Requirements - 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 - Master's degree or PhD in a relevant technical field - 4+ years of experience applying AI solutions or LLMs to real‑world systems (RAG, ranking, classification, reasoning) - Proven expertise in machine learning, statistical methods, and data‑driven problem solving - 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) - Understanding of experimentation methodologies, including offline metrics and online A/B testing - Ability to independently scope problems and deliver high‑quality solutions in ambiguous environments - Strong 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 - Background in search, information retrieval, knowledge graphs, or local/entity understanding - Track record of publications or granted/pending patents - Familiarity with distributed training, model optimization, and production ML infrastructure - Comfort operating across the full lifecycle—from research and prototyping to production and live operations ## Nice to have - (None specified in the original text) ## What we offer - (None specified in the original text) ## About us - (None specified in the original text)