Principal Data Science Manager at Microsoft

Redmond, Washington, United States -

Full Time

Start Date

Immediate

Expiry Date

13 Mar, 26

Salary

274800.0

Posted On

13 Dec, 25

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Science, Statistical Techniques, Python, SQL, Spark, Databricks, Snowflake, BigQuery, Airflow, Kafka, Active Learning, Stakeholder Management, Graph Analytics, Online Experimentation, Team Leadership, Communication

Industry

Software Development

Description

Overview Microsoft is a company where passionate innovators come to collaborate, envision what can be and take their careers further. This is a world of more possibilities, more innovation, more openness, and the sky is the limit thinking in a cloud-enabled world. We are seeking a Principal Data Science Manager to build and lead a hybrid team at the intersection of data labeling/annotation operations and applied data science. Your team will deliver high-quality labeled datasets, active-learning loops that directly improve fraud detection precision/recall, reduce false positives, and speed time-to-mitigation across Microsoft businesses. You will be a leader who sets strategy, hires and develops talent, drives cross-org execution, and ships measurable impact. 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 People leadership & org health: Hire, lead, and develop a blended team of data scientists, label-ops leads, and analytics engineers; foster an inclusive culture and career growth. Strategy & roadmap: Define the labeling/annotation strategy, taxonomy stewardship, and quality framework aligned to fraud risk priorities and partner roadmaps. Active learning & data quality: Design sampling/uncertainty strategies, gold sets, and label accuracy. Programmatic labeling: Introduce fragile supervision, heuristics, and graph-derived signals to pre-label data. Detection enablement: Partner with engineering and data scientists to integrate labels into feature stores, model training, rules evaluation, and shadow tests. Cross-functional influence: Translate ambiguous fraud patterns into clear label definitions and decision rubrics; align with Product, Engineering, and other stakeholders. Executive communication: Report business impact and influence prioritization decisions. Embody our culture and values. 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 technology) 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 technology) OR equivalent experience. Other Requirements Ability to meet Microsoft, customer and/or government security screening requirements are required for this role. These requirements include but are not limited to the following specialized security screenings: Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud background check upon hire/transfer and every two years thereafter. Preferred Qualifications Master’s/PhD in a quantitative field. Hands-on proficiency with Python and SQL; experience with one or more: Spark/Databricks, Snowflake/BigQuery, Airflow, Kafka/PubSub, feature stores, MLFlow. Experience with active learning/uncertainty sampling and human-in-the-loop systems. Stakeholder management and executive communication; ability to set vision and drive cross-org programs to measurable outcomes. Graph analytics/ML and entity-relationship labeling (rings/collusion). Experience with online experimentation and real-time decisioning. Track record building inclusive teams and developing senior ICs/managers. Data Science M5 - 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.

Responsibilities

The Principal Data Science Manager will lead a hybrid team focused on data labeling and applied data science to enhance fraud detection. Responsibilities include defining strategies, fostering team growth, and collaborating with cross-functional teams.