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Microsoft Applied Sciences 
Germany 
234451314

30.07.2024

Required/Minimum Qualifications

    • Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND relevant internship experience (e.g., statistics, predictive analytics, research)
    • OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field OR equivalent experience.

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


Preferred/Additional Qualifications:

  • Experience in clinical language processing is a plus.
  • Experience with localization of natural language processing applications is a plus.

Responsibilities:

Responsibilities include:

  • Contribute to the expansion of DAX Copilot to European countries and languages. Understand modeling paradigms and recipes for US English deployment and reproduce them for other languages. Develop innovative approaches to mitigate limited data resources in new languages by leveraging language translation and large language models
  • Apply deep understanding of machine learning and natural language processing to design and conduct experiments to systematically improve performance metrics including accuracy, latency and cost.
  • Modify and improve existing tools for data processing, experimentation, and analysis of results to enhance researcher experience. Develop an understanding of tools, technologies, and methods being used in the community that can be utilized to improve product quality, performance, or efficiency
  • Work on a component of a product to create impact. Identify approaches, and apply, improve, or create a research-backed solution (e.g., novel, data driven, scalable, extendable) for technique/technology to complete a given task.
  • Ensure ethical and responsible use of AI models and customer data. Apply knowledge of fairness and bias issues in AI research and development. Detect and mitigate potential bias in AI products.
  • Learn to collaborate with the larger team to leverage data to identify pockets of opportunity to apply statistical analysis to improve a solution to a business problem. Uses statistical analysis tools for evaluating machine learning models and validating assumptions about the data.
  • Communicate findings with key product stakeholders (e.g., researchers, engineers, designers, development teams) and understand functions and goals of different teams. Reinforces a positive environment by learning and adopting best practices.