Principal Data Scientist

Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.  In alignment with our Microsoft values, we are committed to cultivating an inclusive work environment for all employees to positively impact our culture every day.

We are the Core Machine Learning (ML) team in Microsoft Feeds & Verticals Org. As a Principal Data Scientist in the Core ML team you will focus on fundamental machine learning techniques including large scale  Deep neural network (DNN) modeling, ranking stack evolvement, large language model utilization, and ranking to support a variety of product needs. This team is the backbone of Microsoft Feeds & Verticals Ranking and dedicated to solving the most challenging ML problems in the org. Join us to make the huge impact! 

Required Qualifications: 

• Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 5+ year(s) data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results)
  • OR Master's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 7+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results)
  • OR Bachelor's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 10+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results)
  • OR equivalent experience.
• 5+ years of industry experience on scalable Machine Learning systems.

Preferred Qualifications: 

• PhD with research experience in machine learning and data science. 
• Passionate about applied machine learning, deep learning, large language models (LLMs) and data science. 
• Proficient skills in system design and ML system design. 
• Publications at Artificial Intelligence conferences (e.g., KDD, CIKM, RecSys, SIGIR, NIPS, CVPR, ICLR, ICML, ACL, etc.) 

Data Science IC5 - The typical base pay range for this role across the U.S. is USD $133,600 - $256,800 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $173,200 - $282,200 per year.

Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here: https://careers.microsoft.com/us/en/us-corporate-pay 

Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, color, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran status, race, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable laws, regulations and ordinances.  We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you need assistance and/or a reasonable accommodation due to a disability during the application or the recruiting process, please send a request via the Accommodation request form.

Benefits/perks listed below may vary depending on the nature of your employment with Microsoft and the country where you work.

#WWE# #MicrosoftFeeds# #FeedsVerticals#

• Lead the planning, design, and execution across a variety of machine learning projects in Feeds & Verticals. 
• Drive innovations in ranking and recommendation algorithms. 
• Tech-Lead team members to grow the technical skills and make impact.