Senior Data Scientist Manager

Do you have a passion for applying Data Science to solve complex problems? Want to apply AI/ML/Copilot using data from Microsoft products like Surface, Gaming, and Augmented-Reality running on Azure Cloud and Edge technologies? We're looking for a Senior Data and Applied Scientist Manager who can develop and deploy advanced analytical and machine learning capabilities within a multi-functional operations organization.

At Microsoft, our mission is to empower every person and every organization on the planet to achieve more. Microsoft Devices Operations (MDO) is responsible for the end-to-end delivery and services of Microsoft's vast array of hardware products including Surface Devices, Xbox Consoles, Accessories, and next-generation products like HoloLens.

As part of Microsoft Devices Operations (MDO), the Data Science Solutions team is developing leading-edge AI experiences to drive improvements across key areas of Cost, Quality, Supply and Services. We streamline, optimize, and automate business processes and decisions by developing and deploying key capabilities leveraging Machine/Deep Learning models, statistical methods, mathematical optimization, simulation, and other advanced techniques.  

The role involves leading a team of data scientists and collaborating with a global diverse team of data scientists and engineers who use data science and MLOps to deliver high-quality AI capabilities. This position requires proficiency in various disciplines, such as data science, data/feature engineering, security auth patterns, MLOps/DevOps, Azure resources, communication skills, and a collaborative work style.

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.

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.
• Minimum 8 years of academic or professional experience in applied Data Science using Python.
• Minimum 5 years of experience in Software Engineering, including deploying solutions at scale using Azure or equivalent cloud platforms/ecosystems.
• Minimum 5 years of experience in leading a team of engineers or scientists, fostering, mentoring career growth.
• High proficiency with data handling and data engineering, running big data solutions like Spark, SQL Serverless or HIVE.

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 Degree in Statistics, Econometrics, Computer Science, Electrical, Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research)
  • OR Doctorate in Statistics, Econometrics, Computer Science, Electrical, Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research)
  • OR equivalent experience.
• 3+ years of people management experience.
• 3+ years experience creating publications (e.g., patents, libraries, peer-reviewed academic papers).
• Experience working within and directing geographically dispersed, diverse, and virtual teams.
• Experience managing both technical and non-technical stakeholders and able to resolve complex business and technical issues.
• Experience in designing and training Deep Learning models such as LSTM, CNN and RNN models.
• Experience in applying Computer Vision, Image Analysis and Object Detection algorithms such as YOLO, SSD and R-CNN.
• Hands-on experience working in a Supply Chain, Manufacturing, Finance, or related roles.
• Experience with PowerBI or another business intelligence platforms.

Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, color, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran status, race, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable laws, regulations and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or the recruiting process, please send a request via the Accommodation request form.

Benefits/perks listed below may vary depending on the nature of your employment with Microsoft and the country where you work.

• Grow and lead a team of Data Scientists and Data Engineers, fostering a culture of continuous learning. 
• Partner with business stakeholders, Program Managers, Engineers, and other Data Scientists to understand business context, identify opportunities, translate business needs into analytical and technical requirements, and deploy solutions.
• Ensure your team adheres to engineering and rigorous data science principles and best-practices, producing high-quality, secure, scalable code and models.
• Evangelize Data Science capabilities and their impact across the broader organization.
• Identify and introduce industry best-practice techniques within AI and Data Science.