Data Scientist
MicrosoftAbout the role
Who we are
Microsoft Development Center Norway (MDCN) is a key engineering hub within Microsoft’s Experiences & Devices division, with offices in Oslo, Trondheim, and Tromsø. Our teams are at the forefront of building and scaling the next generation of Copilot and Large Language Model (LLM) capabilities across Microsoft 365 - powering products like M365 Copilot, Outlook, OneDrive, SharePoint, Skype, and Microsoft Teams.
We work on some of the world’s most advanced distributed systems, operating across hundreds of data centres globally. Our solutions are used by Fortune 500 companies, government agencies, non-profits, and hundreds of millions of users every day.
To deliver on our mission to empower people and organizations to achieve more, we need to leverage data to inform decision making, optimize outcomes and produce innovative solutions and are seeking a skilled data scientist. We can offer an agile development environment with diverse skills in algorithms and data structures, big data, data science, machine learning and search technology to mention a few. The position is full-time and based in our Norway offices. We are looking for engineers who thrive in collaborative environments and are excited to push the boundaries of what AI can do for millions of users worldwide. If you are ready to work on cutting-edge technology with real impact, MDCN is the place for you.
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.
Responsibilities
As a Data Scientist, you will work on some of the world’s largest and most advanced services, leveraging data to improve user experiences, enhance service quality, and drive measurable impact at global scale. You will partner with teams across Microsoft to analyse complex datasets, generate actionable insights, and shape the behaviour of intelligent services that empower millions of users every day.
Responsibilities:
- Apply data analysis, AI, and modelling techniques to understand user behaviour, identify opportunities, and inform product improvements.
- Write robust, reusable, and extensible code to support analysis, modelling, and operationalization at scale.
- Use AI-powered tools in your daily work to accelerate analysis, experimentation, and solution development.
- Develop and maintain metrics and evaluation frameworks to assess the quality, performance, and impact of services.
- Drive data-driven decision making by presenting clear, actionable insights to senior leaders and stakeholders.
- Collaborate in a cross-functional environment with local and remote partners to deliver scalable, elegant, and impactful solutions.
- Acquire, prepare, and validate datasets for analysis and modelling, while contributing to best practices in data collection and data quality.
- Evaluate and improve existing products to ensure intelligent services evolve to meet user needs.
- Design, evaluate, and optimize prompts and fine-tuning strategies for large language models.
Qualifications
Required Qualifications:
- Doctorate 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 Master's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 3+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techn
- OR Bachelor's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 5+ years data-science experience (e.g., managing structured and unstructured data, applying statistical tec
- OR equivalent experien
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