Data Scientist II

Are you passionate about shaping the future applications of AI and empowering millions of users to unlock their full potential? The OneNote team is at the forefront of an exciting transformation with Copilot Notebooks: intelligent, dynamic notebooks infused with powerful AI that act as a true "second brain." Imagine effortlessly capturing ideas, intuitively understanding complex information, and seamlessly taking informed action. This is the heart of our mission.  

As a Data Scientist II working in OneNote team, your core mission is to contribute on Product Analytics/Intelligence, building Data Science models and performing required statistical analysis for Copilot Notebooks and OneNote.
You'll be responsible for understanding various business problems, formulating them with right data science & machine learning techniques and develop required machine learning models to provide insights to enable data driven decision making part of the product analytics/intelligence for the products Copilot Notebook and OneNote.

This opportunity will allow you to work in an exciting and fast-paced environment, collaborating closely with teams across multiple organizations and ship products globally. You will be able to take part in the growth journey of incredible products like Copilot Notebook. You'll have the opportunity to work teams developing the latest models, research and ML techniques that you can bring to the product teams, as well as opportunities to contribute back to the scientific community (via presentations etc).

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.

• Machine Learning Model Development: Design and implement machine learning algorithms to enhance product. This includes developing and evaluating models and preparing large-scale datasets for training and testing.
• Data Analysis & Insights: Analyze usage and interaction data to drive product decisions and improvements to experience.
• Experimentation & Optimization: Design and execute experiments (A/B tests) to evaluate the impact of new features or model changes. Monitor model performance and user engagement metrics and iterate on solutions to optimize outcomes. You will own the end-to-end workflow of data science projects, from hypothesis formulation to deploying validated improvements.
• Cross-Functional Collaboration: Work closely with a multi-disciplinary team of engineers, product managers, researchers, and UX designers in the organization.

Required Qualifications:

• Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field
  • OR Master's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 1+ year(s) data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) or consulting experience
  • OR Bachelor's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 2+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results)
  • OR equivalent experience.
• 3+ years of hands-on experience in data science or a closely related role, building and deploying machine learning models to solve real-world problems.
  
• Deep understanding of machine learning algorithms and statistical techniques. You should be skilled in applying a broad range of methods – including regression, classification, clustering, and recommendation algorithms. Experience in training models for predictive analytics and optimization is highly valued.
  
• Solid programming skills in Python (or equivalent) for data analysis and development. Familiarity with machine learning frameworks and libraries is expected.

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:

• Experience with Azure Analytics stack, e.g., Azure Synapse, Kusto, Power BI
• Good interpersonal and communications (verbal and written) skills, including the ability to effectively communicate with both business and technical teams.
• Proficiency in scenario analytics, mining for insights
• Prior experience in building machine learning models to improve active users for a product
• Prior experience in building models and managing model training, evaluations and integration with production systems
• Proficiency in Python, SQL and commonly used machine learning libraries to develop machine models, time series forecasting, etc.

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.  We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. 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.

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