Microsoft Principal Applied Science Manager 

India, Karnataka, Bengaluru 

732894658

, Office and Windows.

As a Principal Applied Science Managerbe pushing & delivering on multipletight timelihands ondata science activity and work, including training models, creating evaluation sets, building infrastructure for training and evaluation, and more.timezones.

Required Qualifications:

• Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 10+ 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 9+ years related experience (e.g., statistics, predictive analytics, research)
  • OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research).
  • OR equivalent experience.
• 5+ year(s) of people management experience,andat least3+ years of experience delivering team level outcomes.
• 2+ years of industrial experience coding in C++, C#, C,Javaor Python.
• Prior experience with data analysis or understanding, looking at data from alarge-scale systemstoidentifypatterns or create evaluation datasets.

Preferred Qualifications:

• 5+years experiencecreating publications (e.g., patents, libraries, peer-reviewed academic papers).
• 2+years experiencepresenting at conferences or other events in the outside research/industry community as an invited speaker.
• 5+years experienceconducting research as part of a research program (in academic or industry settings).
• 3+years experiencedeveloping and deploying live production systems, as part of a product team.
• 3+years experiencedeveloping and deploying products or systems at multiple points in the product cycle from ideation to shipping.

be responsible forand will engage in:

• Driving projects from design throughconception,implementation, experimentation and finally shipping to our users. This requiresdeep divinginto data toidentifygaps,comingup withheuristics andpossible solutions, using LLMs to create the right model or evaluation prompts, andsetting upthe engineering pipeline or infrastructure to run them.
• Manage a small team of applied scientists, assigning tasks, closelymentoringor guiding them, taking responsibility for their outcome / growth & overall output.Any of the activities below could be delegated or done by the individual oneself – either way, one is expected to know the activity well enough to drive it efficiently.
• Documenting progress & processing, assisting & guiding junior team members, aligning & unblocking them with other stakeholders intimezones.
• Comingup withevaluation techniques, datasets,criteriaand metrics for model evaluations. These are often SOTA models or metrics / datasets.
• Hands on own the fine-tuning, use of language models, including dataset creation, filtering, review, and continuous iteration.