Microsoft Senior Applied Scientist 

Taiwan, Taoyuan City 

379581195

As a Senior Applied Scientist, you will specialize in creating and enhancing machine learning technologies in areas such as natural language processing (NLP), computer vision (CV), and large language models (LLM). You will be a key player within a dynamic team, contributing to and collaborating with other talented colleagues on cutting-edge machine learning challenges from a real ads recommender system.

Required Qualifications:

• Bachelor's Degree in Computer Science, Electrical or Computer Engineering, or related field AND 4+ years related experience (e.g., statistics predictive analytics, research)
  • OR Master's Degree in Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research)
  • OR Doctorate in Computer Science, Electrical or Computer Engineering, or related field AND 1+ year(s) related experience (e.g., statistics, predictive analytics, research)
  • OR equivalent experience.
• 1+ years of experience in recommendation algorithms or natural language processing.

Preferred Qualifications:

• Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research)
  • OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research)
  • OR equivalent experience.
• 3+ years experience creating publications (e.g., patents, libraries, peer-reviewed academic papers).

Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:Microsoft will accept applications for the role until May 26, 2025.

• Building and maintaining production machine learning models for ad retrieval, quality prediction and ad ranking.
• Finding insights and forming hypothesis on web-scale data with various machine learning, feature engineering, statistical, and data mining techniques: e.g. regression, classification, NLP, optimization, p-values analysis.
• Designing experiments, understanding the resulting data, and producing actionable, trustworthy conclusions from them.
• Craft and Optimize Prompts for Effective LLM Performance: Design, test, and refine prompts to elicit accurate, relevant, and useful responses from LLMs. This involves understanding the nuances of how the model interprets different inputs, experimenting with various prompt formulations, and iterating based on performance metrics and user feedback.
• Wrangling large amounts of data (think petabytes) using various tools, including open-source ones and your own.
• Taking complex problems and the associated data and giving the answers in a concise form to assist senior executives in making key business decisions.