Data & Applied Scientist II

The Bing Auto Suggest team is hiring an Data & Applied Scientist II who can help us build the next generation of intelligent experiences that would delight our customers.

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.

Required Qualifications:

• Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field
  • 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 tec
  • 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 tec
  • OR equivalent experience.
• 1+ year(s) customer-facing, project-delivery experience, professional services, and/or consulting experience.

Preferred Qualifications:

• 3+ years of experience in the areas of data science, machine learning, information retrieval or natural language processing.
• Proficiency and demonstrable skills using statistical or machine learning programming languages and packages (Python/R etc.).
• Demonstrable skills and experience using SQL or NoSQL data stores.
• Excellent verbal and written communication skills.

• Have an ability to mine large data sets with Cosmos, Hadoop or Spark like technologies.
• Hands on experience working on different aspects of Generative AI including prompting and finetuning.
• Transform data into innovative features/signals that can improve a machine-learning task.
• Build and productionize ML/DL models and evaluate their quality on real life scenarios.
• Prototype new approaches and develop new algorithms using NLP, ML and DL techniques.
• Have an ability and willingness to develop code (in C#, Python & SQL) to productionize ML techniques.
• Have an ability to self-learn new techniques from textbooks and research papers.
• Never compromise on engineering excellence and delivering quality at scale.