Senior Applied Scientist

Microsoft Microsoft · Big Tech · Bengaluru, KA, IN · Applied Sciences

Senior Applied Scientist role within the Data & Applied Sciences team for Microsoft Teams, focusing on leveraging generative AI, advanced ML, deep learning, and data science to improve product performance and business impact. The role involves developing systems for recommender systems, GenAI applications, and LLM evaluation, with responsibilities spanning data mining, model building, pipeline development, experimentation, and mentoring.

What you'd actually do

  1. Building models and featurization pipelines.
  2. Measuring and improving model performance with feedback loop and reinforcement learning; working with product teams on appropriate designs of AI-powered experiences.
  3. Setting up, running and analyzing experiments.
  4. Prototyping new approaches and developing new algorithms, ML techniques such as modeling using deep learning, advanced ML models and privacy preserving algorithms.
  5. Working with other scientists, engineers and UX experts on the detailed design and implementation of end-to-end solutions, including data-pipelines for machine learning, deployments, performance monitoring and analysis, and continual refinement via feedback loop from real users.

Skills

Required

  • Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ years related experience
  • OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience
  • OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 1+ year(s) related experience
  • OR equivalent experience

Nice to have

  • Ph.D. in Computer Science, Artificial Intelligence, Statistics, Electrical Engineering, Computer Vision, or related field.
  • Publications in top-tier ML/IR/NLP/CV conferences
  • Awareness and understanding of emerging research and technologies.
  • Experience in large scale data mining and cloud computing.
  • Analytical, problem solving, programming and debugging skills.

What the JD emphasized

  • state-of-the-art recommender systems
  • GenAI applications
  • LLM evaluation
  • human communication understanding
  • scalable, distributed and highly efficient components
  • end-to-end solutions

Other signals

  • Generative AI
  • Advanced Machine Learning
  • Deep Learning
  • Data Science
  • Recommender Systems
  • LLM Evaluation