Senior Data Scientist, Product Science

As a Senior Data Scientist, Product Science - you will develop models, algorithms, and metrics that directly influence the operations and strategy of the entire Bing Places organization. Product Science in Bing Places includes modelling the short- and long-term user journeys, identifying drivers of user lifetime value, developing metrics that incentivize user-focused product development, and producing forecasts of key performance indicators. In this role you will learn to influence product strategy with timely research, dive deep into methods of causal analysis, and develop skills in connecting and communicating with engineers, product managers, and executives.

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: 

• Doctorate 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 Master's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 3+ years data-science experience
  • OR Bachelor's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 5+ years data-science experience
  • OR equivalent experience.
• 2+ years of experience (academic or professional) working with large datasets (>1M rows)
• Proficiency in statistical computing and data visualization using Python and/or R

Preferred Qualifications: 

• Proficiency in SQL or a similar database query language
• Experience developing causal analyses on web-scale data
• Demonstrated ability to extract insights from messy, real-world data
• Experience modeling complex systems with techniques such as agent-based modeling (ABM), system dynamics, discrete event simulation (DES), etc.
• Demonstrated ability to influence product roadmaps
  
• Prior role(s) requiring presentation of sophisticated data science work to non-technical audiences

Data Science IC4 - The typical base pay range for this role across the U.S. is USD $117,200 - $229,200 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 $153,600 - $250,200 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

Microsoft will accept applications for the role until November 17, 2024. 

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.

#Bing

• Acquire the data necessary for your project plan and develop usable data sets for modeling. Update internal best practices for data collection and preparation and contribute to data integrity conversations with customers.
• Leverage knowledge of statistical and machine learning techniques (e.g., classification, regression, clustering, forecasting, NLP, etc.) and individual algorithms (e.g., linear and logistic regression, STL decomposition, k-means, gradient boosting, ARIMA) to identify the best approach to complete objectives. You will select the correct approach to prepare data, engineer useful features, train and optimize models, and evaluate the output for statistical and business significance.
• Contribute thought leadership to the product team on the modeling of user journeys and develop novel insights into the features, data, and user cohorts most critical to the long-term success of the business.
• Evaluate your team’s models and recommend improvements as necessary, drive best practices for models, and develop operational models that run at scale. Conduct thorough reviews of data analysis and modeling techniques and identify and invent new evaluation methods.
• Collaborate with engineers, product managers, and executives to develop metrics and forecasts that produce actionable insights for daily monitoring and ship decisions while simultaneously rewarding innovative, user-focused product development.
• Research and maintain a deep knowledge of the industry, including trends and technologies, so that you can identify strategy opportunities and contribute to thought leadership best practices.
• Represent insights from the team to diverse audiences, telling compelling stories about our users and influencing other teams’ analytical approaches.
• Define business, customer, and solution strategy goals, and partner with other teams to identify and explore new opportunities. You’ll also apply a customer-oriented focus to understand their needs and help drive realistic customer expectations.