- Bachelor's Degree in AI, Computer Science, Data Science, Statistics, Physics, Engineering, or a related technical field AND technical engineering experience with coding in languages including, but not limited to, Python and common data libraries (Pandas, NumPy, etc.)
- OR equivalent experience
- Experience in data analysis or data engineering
- Proficiency in statistics and exploratory data analysis methods
- Ability to communicate technical findings effectively to research and product teams
- Master's Degree in Computer Science or related technical field AND technical engineering experience with coding in languages including, but not limited to, Python and common data libraries (Pandas, NumPy, etc.)
- OR equivalent experience.
- Familiarity with data processing frameworks such as Spark, Ray, Apache Beam
- Experience working with large-scale, real-world datasets that are unstructured or semi-structured
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.
#copilot #microsoftAI
- Create high-quality datasets for training and evaluation; run experiments on new datasets (data ablations) to assess their impact and determine the most effective data
- Develop and maintain scalable data pipelines for multimodal ingestion, preprocessing, filtering, and annotation
- Analyze real-world multimodal datasets to assess quality, diversity, relevance, and identify areas for improvement
- Build lightweight tools and workflows for dataset auditing, visualization, and versioning
- Collaborate with Safety, Ethics, and Governance teams to ensure datasets meet standards for quality, privacy, and responsible AI practices
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