The mission of Microsoft Digital Security & Resilience (DSR) is to enable Microsoft to build the most trusted devices and services, while keeping our company safe and our data protected. As part of the Microsoft Security organization, and a steward of Microsoft and our customer’s data, a core function of Microsoft DSR is ensuring the security of every aspect of the business. Microsoft DSR is responsible for company-wide information security and compliance, with a strategic focus on information protection, assessment, awareness, governance, and enterprise business continuity. As customer zero, we deploy and secure these services inside Microsoft and then share best practices with enterprise customers at scale across the globe. We have exciting opportunities for you to innovate, influence, transform, inspire and grow within our organization and we encourage you to apply to learn more!
Are you passionate about the idea of protecting over a billion people and making the world a durably safer place? The Microsoft Security Response Center (MSRC) is on the forefront of protecting the breadth of Microsoft’s customers from emerging threats to security and privacy.
The MSRC Data Science team is responsible for building data pipelines, data mining, Machine Learning (ML) models and insights on security related data. We combine our data science work with business and engineering knowledge to provide unique insights into customer scenarios that are leading the data-driven culture within security. We are looking for a hands-on Senior Data and Applied Scientist with experience to build reliable products and make business impact using data analysis, machine learning, experiments, data mining, data visualization, and more.
We are looking for a Senior Data and Applied Scientist to partner with a wide range of engineers, program managers, and security researchers, build and deliver solutions using Machine Learning and Statistics. This person should be able to own one or more areas of opportunities and identify business or engineering problems, dig out sources of data, conduct the analysis that would reveal useful insights, and eventually help engineering teams to operationalize data-driven solutions.
Responsibilities- Leverage subject matter expertise to analyze problems and issues facing projects to uncover, manage, and/or mitigate factors that can influence final outcomes across product lines. Partner with the business team to drive strategy and recommend improvements.
- Independently write efficient, readable, extensible code/model that spans multiple features/solutions. Contribute to the code/model review process by providing feedback and suggestions for implementation and improvement.
- Lead and scope out large quantitative projects and translate them into a machine learning problem and think of optimal ways of solving it.
- Identify data sources, integrate multiple sources or types of data, apply expertise within a data source to develop methods to compensate for limitations and extend the applicability of data.
- Transform formulated problems into implementation plans for experiments by applying (and creating when necessary) the appropriate methods, algorithms, and tools, and statistically validating the results against biases and errors.
- Use broad knowledge of Machine Learning and Deep Learning innovative methods, algorithms, and tools from within Microsoft and from the scientific literature.
- Interpret data and communicate in a clear and lucid way to a wide variety of audiences.
- Validate, monitor, and drive continuous improvement to methods, and propose enhancements to data sources that improve usability and results.
- Mentor early-in-career teammates and establish high standards in both data science and engineering excellence.
- Keep up to speed with the current academic and industry advances in machine learning techniques, experiment with their application to improve our ML models.
- Work in collaboration with teammates to ensure reliable and trustworthy data for critical business decisions to improve reliability, scalability, and efficiency.
QualificationsRequired/Minimum 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.
Other RequirementsAbility to meet Microsoft, customer and/or government security screening requirements are required for this role.
Additional Or Preferred Qualifications- 2+ years experience leveraging knowledge of machine learning solutions (e.g., classification, regression, clustering, forecasting, natural language processing [NLP], image recognition).
- 2+ years experience in modeling techniques (e.g., dimensionality reduction, cross-validation, regularization, encoding, assembling, activation functions).
- 2+ years’ experience in building data pipelines using cloud computing like Python, Azure SQL database, Kusto (Azure Data Explorer), Azure ML, Azure Key Vault, Azure Storage or similar.
Data Science IC4 - The typical base pay range for this role across the U.S. is USD $112,000 - $218,400 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 $145,800 - $238,600 per year.
Microsoft is an equal opportunity employer. Consistent with applicable law, 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.
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