Senior Applied Scientist

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Microsoft's mission is to empower every person and every organization on the planet to achieve more and we believe that artificial intelligence will play a critical role in accomplishing that mission. The Core Search and AI team is the leading applied machine learning team at Microsoft responsible for delivering the highest-quality search experience to over 500M+ monthly active users around the world in Microsoft’s search engine, Bing and other dependent search engines such as Yahoo, DuckDuckGo, and new startups like Neeva.

We have seen little innovation in search in the last decade and we are looking for people who want to build the next generation of search using advanced AI technologies at scale. We are responsible for the largest machine learning models at Microsoft by volume and take pride in being the first in the world to solve many practical AI at Scale challenges. Our work spans a very large scope of scenarios including delivering high quality search results from a massive document corpus, extractive and abstractive summarization to generate document snippets, personalization, machine reading comprehension, and document recommendations.

As a team, we leverage the diverse backgrounds and experiences of passionate engineers, scientists, and program managers to help us realize our goal of making the world smarter and more productive. We believe great products are built by inclusive teams of customer-obsessed individuals who trust each other and work together closely. We collaborate regularly across the company both to find the technology breakthroughs from groups like Microsoft Research to infusing AI into the rest of Microsoft products like Office and Azure.

Required Qualifications:

Bachelor's Degree in Statistics, Econometrics, 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 Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 1+ year(s) related experience (e.g., statistics, predictive analytics, research) OR equivalent experience.

• 2+ years of industry data science experience
• 4+ years of experience with data science programming tools such as R, SQL, and Excel

Preferred / Additional 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.

• Experience with machine learning frameworks (e.g., TensorFlow, PyTorch)
• Experience building large-scale, cloud-based solutions
• Strong customer focus, strategic mindset, and ability to drive results
• Self-motivated with a strong bias for action
• Exceptional problem-solving skills—ability to tackle challenges that have never been solved before

• Apply state-of-the-art research and advanced algorithms to develop scalable, data-driven solutions that deliver measurable product impact.
• Collaborate with product, engineering, and research teams to transfer technology and integrate innovative approaches into production systems.
• Design and optimize machine learning models and data pipelines for large-scale applications, ensuring performance, scalability, and ethical standards.
• Mentor and guide less experienced team members, fostering technical growth and sharing best practices across projects.
• Stay current with industry trends and emerging technologies; publish research and share insights to advance innovation and business impact.
• Incorporate fairness, bias detection, and privacy considerations into research and product development processes.
• Drive improvements in data quality and leverage advanced analytics to identify opportunities for product enhancement.