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职位Microsoft

Principal, Data Science & Analytics

Microsoft

Principal, Data Science & Analytics

Microsoft

United States, Washington, Redmond; United States, California, Mountain View

·

On-site

·

Full-time

·

1mo ago

必备技能

Machine Learning

Overview:

Microsoft AI (MAI) builds an integrated consumer AI ecosystem across search, browsing, and content, focused on delivering trustworthy, scalable experiences with durable user and business value. The MAI Ecosystem Data Science Team owns MAI‑wide metrics, shared measurement systems, and experimentation frameworks to enable consistent, high‑confidence decisions and optimize MAI‑level outcomes.

We are seeking a Principal, Data Science & Analytics for ecosystem data science to own cross product measurement strategy, partner across product and business teams, and uphold a high bar for metric quality, statistical rigor, and data driven leadership.

We are looking for high-energy Data Scientist, creative modeling geeks who are willing to work in a dynamic environment to solve real life day to day problems, leveraging data science techniques. You will enjoy and be successful in this role if you are curious and willing to challenge the status quo and come up with data driven solutions to ambiguous problems.

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:

As a Principal, Data Science and Analytics in the team, your major responsibilities include:

  • Leadership: Mentor data scientists and align work with MAI ecosystem goals, driving technical excellence, innovation, and cross-team collaboration.
  • Data Strategy & Execution: Develop ecosystem data strategies for marketplace and system performance, including standardized data collection, analysis, reporting, and interpretation; validate analytical approaches and results.
  • Advanced Analytics & Measurement: Apply machine learning, statistical modeling, data mining, and experimentation to large datasets; define and deliver metrics that accurately measure user and business value across products and marketplace components.
  • Experimental Design & Implementation: Design and execute experiments across user and demand dimensions; translate strategy into clear, actionable, and measurable plans, sharing progress and results with stakeholders.
  • Collaboration: Partner closely with product, program management, engineering, and business teams to integrate data science solutions into shared platforms and marketplace operations.
  • Performance Optimization: Identify cross-team opportunities for product and process improvement; implement data-driven solutions to improve efficiency, reliability, and user experience.
  • Influence & Decision-Making: Engage stakeholders with clear, compelling, and actionable insights; make independent decisions for the team and handle complex tradeoffs to drive product and service improvements.
  • Technical & Operational Leadership: Develop and standardize processes for data acquisition, quality, and operationalizing ML models; provide expert review of analysis and modeling; lead adoption of new tools and technologies to improve availability, reliability, efficiency, and performance.
  • Standards & Trusted Advisory: Establish and uphold standards, policies, and best practices for high-quality, efficient, and extensible code; influence business, customer, and solution strategy with a strong customer focus; act as a trusted advisor across the ecosystem.

Qualifications:

Required Qualifications:

  • Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 5+ years 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.

Preferred Qualifications:

  • Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 8+ years 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 10+ 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 12+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results)
  • OR equivalent experience.

#MicrosoftAI

Data Science IC5 - The typical base pay range for this role across the U.S. is USD $139,900 - $274,800 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 $188,000 - $304,200 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

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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关于Microsoft

Microsoft

Microsoft

Public

Microsoft Corporation is an American multinational technology conglomerate headquartered in Redmond, Washington.

10,001+

员工数

Redmond

总部位置

$3000B

企业估值

评价

3.8

5条评价

工作生活平衡

4.1

薪酬

4.3

企业文化

3.4

职业发展

3.2

管理层

3.0

65%

推荐给朋友

优点

Excellent compensation and benefits package

Four-day workweek with improved work-life balance

Supportive managers and teams

缺点

High-pressure environment causing anxiety

Unprofessional interview processes

Limited creative work opportunities

薪资范围

5,620个数据点

Mid/L4

Principal/L7

Senior/L5

Staff/L6

Director

Mid/L4 · Applied Science

1份报告

$234,166

年薪总额

基本工资

$180,128

股票

-

奖金

-

$234,166

$234,166

面试经验

1次面试

难度

4.0

/ 5

时长

14-28周

体验

正面 0%

中性 0%

负面 100%

面试流程

1

Application Review

2

Recruiter Screen

3

Technical Phone Screen

4

Onsite/Virtual Interviews

5

Team Matching

6

Offer

常见问题

Coding/Algorithm

System Design

Behavioral/STAR

Technical Knowledge

Culture Fit