Microsoft Data Applied Scientist 

United States, Washington 

92709102

Required Qualifications:

• Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field

  o OR Master's Degree 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 consulting experience

  o OR Bachelor's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 2+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results)

  o OR equivalent experience.

• Experience
  • in customer-facing, project-delivery, professional services, and/or consulting,
  • AND with open source machine learning library such as Scikit-Learn, Pandas, Seaborn and or similar,
  • AND with statistics/machine learning models or LLM models such as linear/nonlinear regression, Bagged and Boosted Trees, Bayes methods, Transformer and or similar.
• Experience in
  • Anomaly detection algorithm design and implementations experience or online experiments design and implementations,
  • AND technical engineering experience with coding in languages including, but not limited to, C++, C#, Java, JavaScript, or Python OR equivalent experience.

Other Qualifications:

• 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 Cloud Background Check upon hire/transfer and every two years thereafter.

Preferred Qualifications:

• Bachelor's Degree in Computer Science, Data Science, Applied Mathematical or Statistics, or related field AND 5+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results)
  
  • OR Master's Degree in Computer Science, Data Science, Applied Mathematical or Statistics, or related field AND 3+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results)
  • OR Doctorate in Computer Science, Data Science, Applied Mathematical or Statistics, or related field AND 1+ year(s) data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results)
  • OR equivalent experience.
• 5+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience.
• Top Tier conference/Journal publications related to anomaly detection, auto-diagnosis and experiments design and implementations.
• Deep compute science knowledge (concepts such as CPU/Memory/TOR/ LB/Vnet/ VLan etc), and serverless architectures and other cloud architectural patterns.
• Familiar with data visualization tools such as PowerBI, Networkx or similar.

Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:

• Share accountability of a wide array of assets and be comfortable with learning a broad array of technologies.
• Independently design and implement anomaly detection, auto-triaging/correlation, and causal inference model to deliver preventive insights to improve Azure cloud system availability, reliability, and efficiency
• Work with partner teams to integrate the Insights into Azure daily dev operations and Azure system for automatic mitigation and repairs
• Contribute towards driving visibility into customer impacting on Virtual Machines or Containers or higher-level Azure services built on top of Virtual Machines.
• Assist with building an automated data quality solution to detect problems in downstream dependencies and take automated action to correct them.
• Look for opportunities to share learnings and tools broadly within Microsoft and beyond. Specifically, our team does cutting edge work with Azure Data Explorer (Kusto) and makes a point at contributing back to the larger environment.