Senior Applied Scientist(Feeds and AI team)

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In Feeds and AI team, we’re building Microsoft Start, a content powerhouse for every person on the planet to be informed, entertained, and inspired. Microsoft Start is the place for all to read, watch, listen, create, connect with people of shared interests, and experience wonders in the world.

We are looking for a skilled and experienced Senior Applied Scientist to join our ranking foundation team. As a Senior Applied Scientist focusing on recommender systems, you will play an important role in contributing to the development and optimization of our online services and offline pipelines within our content ecosystem to achieve product and business growth goals.

• Master's degree in Computer Science, Statistics, Data Science, or related field, with solid background in machine learning, data mining or related applied science.
• 3 years of work experience in recommender systems, search engine, or online advertising, with rich experience on machine learning algorithms, generative AI / LLMs, statistics, data mining techniques, and their application on personalization.
• Excellent problem-solving skills and ability to work in a fast-paced, collaborative environment. Strong communication and teamwork skills, with the ability to effectively present and explain technical concepts to diverse audiences.
• Strong programming skills in Python and experience with other programming languages like C#, C++ is a plus.

The primary responsibilities will include:
• Algorithm Development and Enhancement for ranking algorithms in News & Feeds
- Work with cross-functional teams to design, develop, and implement recommendation algorithms to deliver product features and drive user engagement.
- Optimize existing recommendation algorithms by analyzing performance metrics and user feedback, incorporating advanced machine learning techniques including generative AI techniques.
• Innovation in the area of NLP, LLM, and recommender system.
• Data Analysis and Modeling
- Perform data analysis to identify patterns, trends, and opportunities to improve the relevance and quality of our recommendation systems.
- Build systemic solutions and models to optimize user experience.