Microsoft Principal Applied Science Manager 

Taiwan, Taoyuan City 

1645770

As a

Required Qualifications:

• Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 15+ 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 8+ 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.
• 6+ year(s) of people management experience.
• 5+ years of experience delivering team level outcomes.
  
• 8+ years of industrial experience coding in C++, C#, C, Java or Python
  
• Prior experience with data analysis or understanding, looking at data from a large-scale systems to identify patterns or create evaluation datasets
  
• Familiarity with common machine learning, deep learning frameworks and concepts, using use of LLMs, prompting. Experience in pytorch or tensorflow is a bonus.
  
• Ability to communicate technical details clearly across organizational boundaries.

Preferred Qualifications:

• 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+ years of people management experience.

• Driving projects from design through conception, implementation, experimentation and finally shipping to our users. This requires deep diving into data to identify gaps, coming up with heuristics and possible solutions, using LLMs to create the right model or evaluation prompts, and setting up the engineering pipeline or infrastructure to run them.
• Manage a small team of applied scientists, assigning tasks, closely mentoring or 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 in timezones.
• Coming up with evaluation techniques, datasets, criteria and 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.