Principal Data Scientist- M365 Security Engineering

Are you passionate about leveraging data science to protect customer data and solving security challenges?

Are you driven to identify unusual patterns and detect security attacks from large-scale data?

Do you thrive on the challenge of creating invisible security solutions at scale?

Are you interested in solving complex problems using distributed computing, hybrid architectures, and cloud technologies?

If so, the M365 Security Engineering team at Microsoft has a fantastic opportunity for you. We are seeking a Principal Data Scientist to join our team and work on cutting-edge solutions that safeguard Microsoft services against cyberattacks. The Principal Data Scientist will play a critical role in enhancing our security posture by developing innovative models to detect and predict security threats. This role requires a deep understanding of data science, machine learning, and cybersecurity. The ideal candidate will have extensive experience in analysing large datasets, developing algorithms, and working closely with security experts to understand emerging threats and vulnerabilities.

Our team values diversity in all its forms and believes in deep collaboration to harness the best of technology. We are a group of Security Engineers, Software Developers and Data Scientists with expertise in large-scale software systems, security analysis, big data, and machine learning. We delight in diving deep into the billions of events and terabytes of data generated daily by Microsoft products and services (e.g., Azure, M365) to detect and respond to suspicious activities. We ensure that critical security components are embedded throughout our infrastructure and are continuously.

• Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 5+ 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 7+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results)
  • OR Bachelor's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 10+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results)
  • OR equivalent experience.
• 8+ years of experience in Data Science, machine learning, natural language processing, and deep learning preferably with a focus on security or related fields.
• Experience in programming languages such as Python, R, or Scala, with hands-on experience in data analysis, experimental design principles and visualization.
• Experience in translating complex data into actionable insights and recommendations that drive business impact.
• Excellent technical design skills and proven ability to drive large scale system designs for complex projects or products.  
• Expertise in machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, scikit-learn).
• In-depth knowledge of cybersecurity principles, threats, and attack vectors.
• Experience with big data technologies (e.g., Hadoop, Spark, Kafka) and data processing.
• Strong analytical and problem-solving skills with the ability to think creatively.
• Excellent communication skills with the ability to explain complex concepts to non-technical stakeholders.

Preferred Qualifications:

• Experience in developing and deploying machine learning models for security applications.
• Experience in Big Data preferably in the cybersecurity or SaaS industry.
• Experience with data science workloads with the Azure tech stack; Synapse, Azure ML, etc.
• Knowledge of anomaly detection, fraud detection, and other related areas.
• Familiarity with security fundamentals and attack vectors.
• Publications or contributions to the field of data science or cybersecurity.
• Excellent track record of cross team collaboration.
• Ambitious, self-motivated. 
• Agile, can-do attitude and great at dealing with ambiguity. 

Background Check Requirements: 

• The ability to meet Microsoft, customer and/or Government security screening requirements is 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 higher/transfer and every 2 years they are after.

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. 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.

• Develop and implement machine learning models and algorithms to detect security threats and attacks within M365 services.
• Analyse large and complex datasets generated by M365 to identify patterns and anomalies indicative of security risks.
• Collaborate with security experts to understand threat landscapes and incorporate domain knowledge into models.
• Continuously monitor and improve the performance of security models to adapt to evolving threats.
• Lead the design and implementation of data-driven security solutions and tools.
• Mentor and guide junior data scientists in best practices and advanced techniques.
• Communicate findings and insights to stakeholders, including senior leadership and technical teams.
• Stay up to date with the latest advancements in data science, machine learning, and cybersecurity.