The Microsoft Search in Bing Team is hiring Applied Scientist II in Redmond, WA , with a keen interest in building state-of-the-art machine learning models. In this role, you will have the opportunity to collaborate with teams across Microsoft and the world to develop innovative search and chat solutions. 

You will be part of a growing team who take pride in their work, and care about their colleagues.

If you are someone who takes ownership of your projects and thrives in a dynamic environment, we would love to consider your application for this role.

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.

Responsibilities

• Create and prototype machine learning and natural language processing models aimed at enhancing search relevance and quality.

• Dive deep into user behaviors, analyze patterns, and conduct research to improve the end-to-end search and chat experience.

• Work closely with a cross-functional team of scientists and engineers to architect and develop operational models that can scale efficiently.

Embody our Culture (https://www.microsoft.com/en-us/about/corporate-values) and Values (https://careers.microsoft.com/us/en/culture)

Qualifications

Required 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 AND 1+ year(s) related experience (e.g., statistics, predictive analytics, research)

• OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field

• OR equivalent experience.

• 2+ years of professional experience in application of machine learning.

• 2+ years of experience in at least one programming language such as Python/R/C#/Java/C++.

Preferred Qualifications:

• 2+ years of experience with Natural Language Processing.

• Effective communication and collaborative skills.

• Online service knowledge, especially in areas like search.

• Experience in handling analytic and big data tasks.

• PhD in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering preferred.

Applied Sciences IC3 - The typical base pay range for this role across the U.S. is USD $94,300 - $182,600 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 $120,900 - $198,600 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 will accept applications and processes offers for these roles on an ongoing basis.

Fullstack# #Backend# #Frontend# #MachineLearning #Cloud# #WebXT# #nextplay

Microsoft is an equal opportunity employer. Consistent with applicable law, all qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations (https://careers.microsoft.com/v2/global/en/accessibility.html) .