Jobs
Required Skills
Machine Learning
Statistics
Predictive Analytics
Overview
This role is part of the Microsoft Search & Ads Network (MSAN) modeling team, focused on building large-scale machine learning systems for ads retrieval, ranking, and marketplace optimization across different surfaces. The team develops end-to-end models that predict user engagement and advertiser value—powering candidate generation, relevance scoring, and serving stack ranking that directly impact ad quality, delivery efficiency, and revenue. Responsibilities span the full modeling lifecycle, including training data and labeling strategy, feature and signal design, model development, and rigorous offline and online evaluation. Engineers and applied scientists work closely at the intersection of machine learning, economics, and large-scale systems to deliver high-performance real-time inference and robust experimentation in production.
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.
Starting January 26, 2026, Microsoft AI (MAI) employees who live within a 50- mile commute of a designated Microsoft office in the U.S. or 25-mile commute of a non-U.S., country-specific location are expected to work from the office at least four days per week. This expectation is subject to local law and may vary by jurisdiction.
- Responsibilities- Have a solid background in Machine Learning, Reinforcement Learning, Causal Inference, Data Science, Data Mining, or related field.
- Be passionate about artificial intelligence and optimization at web scale.
- Play a key role in driving algorithmic improvements to online and offline systems, develop and deliver robust and scalable solutions, make direct impact to both user and advertisers experience, and continually increase the revenue for Bing ads.
Qualifications 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.
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.
- Research experience (publications) in the following areas: statistical machine learning, deep learning, data mining, causal inference, information retrieval, and Bayesian inference.
- 2+ years of experience in any of the following areas: statistical machine learning, deep learning, data mining, causal inference, information retrieval, game theory, mechanism design, optimization and Bayesian inference.
- Proficient problem solving and data analysis skills.
- Proficient software design and development skills/experience.
#MicrosoftAI
Applied Sciences IC4 - The typical base pay range for this role across the U.S. is USD $119,800 - $234,700 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 $158,400 - $258,000 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
This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled.
Microsoft is an equal opportunity employer. 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 with religious accommodations and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.
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About Microsoft
Reviews
3.8
5 reviews
Work Life Balance
4.1
Compensation
4.3
Culture
3.4
Career
3.2
Management
3.0
65%
Recommend to a Friend
Pros
Excellent compensation and benefits package
Four-day workweek with improved work-life balance
Supportive managers and teams
Cons
High-pressure environment causing anxiety
Unprofessional interview processes
Limited creative work opportunities
Salary Ranges
5,571 data points
Mid/L4
Principal/L7
Senior/L5
Staff/L6
Director
Mid/L4 · Data and Applied Scientist
0 reports
$202,099
total / year
Base
$149,342
Stock
$32,252
Bonus
$20,505
$139,572
$301,212
Interview Experience
7 interviews
Difficulty
3.7
/ 5
Duration
14-28 weeks
Offer Rate
14%
Experience
Positive 14%
Neutral 29%
Negative 57%
Interview Process
1
Application Review
2
Recruiter Screen
3
Technical Phone Screen
4
Technical Interview
5
Onsite/Virtual Interviews
6
Final Round
7
Offer
Common Questions
Coding/Algorithm
System Design
Behavioral/STAR
Technical Knowledge
Past Experience
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