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Microsoft Applied Scientist Microsoft AI Development Acceleration Program Cambridge 
Taiwan, Taoyuan City 
352287086

Yesterday

Required Qualifications:

  • Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND relevant internship experience (e.g., statistics, predictive analytics, research)
    • OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field
    • OR equivalent experience.
  • Candidates must be available to start full-time in July 2026.

Preferred Qualifications:

  • 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
    • OR equivalent experience.
  • Experience in Machine Learning/statistics/LLMs/SLMs/multi-modal models/probabilistic graphical models/Bayesian networks/deep learning/reinforcement learning/time series/active learning, or other modeling paradigms.
  • Experience in Python/R/Scala or similar.
  • Energized by creating AI solutions and the prospect of working on a wide variety of datasets and AI applications, across many products and engineering teams.
  • Research publications are a plus.

Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:

Responsibilities
  • Research, develop, and implement AI solutions in application projects for Microsoft’s products and services.
  • Select and apply appropriate statistical and machine learning techniques to large-scale, high-dimensional data.
  • Stay current with the latest research and technology and communicate your knowledge throughout the organization.
  • Take responsibility for preparing data for analysis, reviewing data preparation/ETL code, and providing critical feedback on issues of data integrity.
  • Share knowledge by clearly articulating results and ideas to customers, managers, and key decision makers.