Applied Scientist II

Security 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 Central Fraud and Abuse Risk (CFAR) team builds innovative, intelligent, and scalable risk solutions that protect Microsoft’s customers and services from abuse and fraud. We combine deep security expertise, high-quality data, and engineering excellence to enable real-time and strategic decision-making. We value inclusivity, experimentation, collaboration, and a growth mindset. 

Do you have a passion for data and machine learning? Do you want to apply it to create security services that help millions of customers protect their applications from threat actors around the globe? Are you curious, analytical, and motivated by new challenges and willing to prove it? If so, we would like to hear from you! 

We are developing a cloud scale service that provides detections for fraud and abuse activities across Microsoft Clouds. This solution is based on state-of-the-art machine learning and other techniques that rely on aggregating large-scale information. The team uses various detection strategies to identify evolving fraud patterns to combat fraud and abuse. 

We are seeking an Applied Scientist to help us grow and modernize our data science and detection strategies. As an Applied scientist on our team, you will partner with our engineers, other data scientists, and researchers to design, build, and deploy state-of-the-art machine learning to protect Microsoft Clouds. In your day-to-day work, you will build machine learning pipelines, vet experiments, incorporate quality monitors, and ship successful models to production. 

You will have the opportunity to collaborate with partners across Microsoft who have decades of security research, and ML expertise. Microsoft provides phenomenal community, tools, and technologies to grow your expertise in this field.  

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 2+ 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 1+ year(s) related experience (e.g., statistics, predictive analytics, research)  
  • OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field OR equivalent experience.
• 2+ years of Data Science or Machine Learning experience in handling high volumes of structured and unstructured data 
• 2+ years of extensive programming experience in at least one of the following languages: C#, Python, or similar language 
• 2+ years of data querying experience in SQL, Kusto (KQL) or similar languages 

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 background and Microsoft Cloud background check upon hire/transfer and every two years thereafter.

Preferred Qualifications: 

• Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 5+ 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. 
• 1+ year(s) experience creating publications (e.g., patents, peer-reviewed academic papers). 
• Familiarity with ML tools & frameworks such as PySpark, Scikit-Learn, PyTorch etc. 
• Familiarity with experiment design and applied machine learning for predicting outcomes in large-scale, complex datasets. 
• Experience in cloud services with prior exposure to Big Data technologies is desirable but not required. 
• Security and Fraud domain experience and interest

Applied Sciences IC3 - The typical base pay range for this role across the U.S. is USD $100,600 - $199,000 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 $131,400 - $215,400 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 September 7, 2025.

#CFAR #Fraud #Abuse #DataScience #AppliedScience #MachineLearning #AI

• Serve as a domain expert in machine learning, statistics, and experiment design. 
• Drive fraud detection by researching evolving behaviors, identifying features with threat analysts, and applying scalable detection strategies. 
• Collaborate cross-functionally with engineering and security teams to operationalize data and feature pipelines for new scenarios. 
• Own model lifecycle by monitoring deployed detection models and continuously enhancing detection coverage and reliability. 

Embody our culture and values