New 
Senior Data Scientist
 Microsoft | |
 United States, Washington, Redmond | |
 Nov 29, 2024 | |
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OverviewSecurity represents the most critical priorities for our customers in a world awash in digital threats, regulatory scrutiny, and estate complexity. Microsoft Security aspires to make the world a safer place for all. We want to reshape security and empower every user, customer, and developer with a security cloud that protects them with end to end, simplified solutions. The Microsoft Security organization accelerates Microsoft's mission and bold ambitions to ensure that our company and industry is securing digital technology platforms, devices, and clouds in our customers' heterogeneous environments, as well as ensuring the security of our own internal estate. Our culture is centered on embracing a growth mindset, a theme of inspiring excellence, and encouraging teams and leaders to bring their best each day. In doing so, we create life-changing innovations that impact billions of lives around the world. The MSRC Data Science team is responsible in 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, data engineering, Machine learning, GenAI, data mining, data visualization, and more. The role will partner with a wide range of engineers, program managers, and security researchers. Not limited to the mentioned problem space, 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. 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. In alignment with our Microsoft values, we are committed to cultivating an inclusive work environment for all employees to positively impact our culture every day.
ResponsibilitiesLeverage 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 business team to drive strategy and recommend improvements. Raise opportunities to look for new work opportunities and different contexts to use existing work. Establishes, applies, and teaches standards and best practices. * Independently write efficient and scalable solutions using Data Engineering and Machine Learning. Contribute to the code/model review process by providing feedback and suggestions for implementation and improvement. Develops expertise in proper modeling, coding, and/or debugging techniques such as locating, isolating, and resolving errors and/or defects. * Lead and Scope out large quantitative projects and translate it into a machine learning problem and think of optimal ways of solving it. Outline alternative approaches and identify pros, cons, risks and provide recommended approaches. * 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. * Interpret data and communicate in a clear and lucid way to a wide variety of audiences. * Mentor early-in-career teammates and establish high standards in both data science and engineering excellence. * Work in collaboration with teammates to ensure reliable and trust-worthy data for critical business decisions. | |