Microsoft Principal Data Science Manager 

Taiwan, Taoyuan City 

500248703

organization delivers and operates the safest, most available, and secure cloud in the industry. In support of this mission, theCloud Applied AI (CAAI)

This leader will guide a diverse team of data scientists working on a broad portfolio of initiatives, including:

• Datacenter construction
• Safety & security
• Delivery planning & execution
• Infrastructure optimization
• Critical operations

This individual will leverage their experience in data science while staying abreast of the latest research and developments to identify innovative approaches to solving challenges at scale. This leader will collaborate with engineering and product counterparts to deploy these solutions in production environments supporting Microsoft’s datacenters globally.

• This role is located either in one or all hub locations - Atlanta, GA, Washington, D.C., Redmond, WA, San Antonio, TX or Phoenix, AZ.
• Relocation support will be provided, and successful candidates must ​relocate or reside within 50 miles of the hub office location.
• This role is eligible for hybrid or remote work, up to (XX indicated by HM)%.

Required/Minimum Qualifications

• Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 8+ years related experience (e.g., statistics, predictive analytics, research)
  • ORMaster's Degreein Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research)
  • ORDoctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 5+ years related experience (e.g., statistics, predictive analytics, research)
  • ORequivalent experience.
• 3+yearspeople management experience.
• 8+ years of experience in data science developing and deploying models and solutions with a focus on data platforms, machine learning, and analytics
  • 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.

Additional or Preferred Qualifications

• Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 12+ years related experience (e.g., statistics, predictive analytics, research)
• ORDoctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 8+ years related experience (e.g., statistics, predictive analytics, research)
• OR equivalent experience.
• 7+ years people management experience.
• 3+ years experience presenting at conferences or other events in the outside research/industry community as an invited speaker.
• 7+ years experience conducting research as part of a research program (in academic or industry settings).
• 5+ years experience developing and deploying live production systems, as part of a product team.
• 7+ years experience developing and deploying products or systems at multiple points in the product cycle from ideation to shipping.
• Experience in datacenter operations, manufacturing, or similar operational environments.
• Experience leveraging generative AI to optimize processes or enhance software systems

Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:Microsoft will accept applications for the role until March 12, 2025.

• Lead and manage the data science team, ensuring the delivery of high-quality data science models and solutions.
• Collaborate with senior leaders to define and execute the data science strategy.
• Develop and implement data science models that measurably contribute to organizational goals.
• Work closely with cross-functional teams to identify and prioritize data science projects.
• Drive continuous improvement in data science processes and methodologies.
• Mentor and develop team members, fostering a culture of innovation and excellence.
• Stay current with the latest advancements in data science and machine learning, and apply them to solve business problems.
• Hire, retain, and develop top talent, ensuring the team has the skills and expertise needed to meet current and future challenges.
• Promote diversity and inclusion within the team, creating an environment where all team members feel valued and supported.