Senior Data & Applied Scientist

We are looking for a Senior Data & Applied Scientist who wants to build the next generation of search to revolutionize the way people discover and access information by harnessing the power of advanced language models and search technologies, ensuring a personalized, reliable, and inclusive search experience for all users. We are responsible for the largest machine learning models at Microsoft by volume and take pride in being the first in the world to solve many practical AI at Scale challenges. Our work spans a very large scope of scenarios including delivering high quality search results from a massive document corpus, extractive and abstractive summarization to generate document snippets, machine reading comprehension, and document recommendations.

As a team, we leverage the diverse backgrounds and experiences of passionate engineers, scientists, and program managers to help us realize our goal of making the world smarter and more productive. We believe great products are built by inclusive teams of customer-obsessed individuals who trust each other and work together closely.  We collaborate regularly across the company both to find the technology breakthroughs from groups like Microsoft Research to infusing AI into the rest of Microsoft products like Office and Azure.

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

• Define, invent, and deliver behavioral and human labeled metrics which accurately measure the satisfaction and success of our customers.
• Research, analyze, and decide between various sampling strategies which accurately reflect user perception.
• Lead a team to design a blueprint for how to get high quality labels with latest technologies and expert judges.
• Enable other machine learning scientists in the broader group to experiment quickly and accurately.
• Make independent decisions for the team and handle difficult tradeoffs.
• Translate strategy into plans that are clear and measurable, with progress shared out monthly to stakeholders.
• Partner effectively with program management, engineers, and other areas of the business.

Qualifications

Required Qualifications:

• Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ years related experience (e.g., statistics predictive analytics, research)
  • OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research)
  • OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 1+ year(s) related experience (e.g., statistics, predictive analytics, research)
  • OR equivalent experience.
• 2+ years experience in Python.
• 2+ years experience in designing and implementing search engine metrics, preferably with projects that involve LLMs.

Additional or Preferred Qualifications:

• 3+ years customer-facing, project-delivery experience, professional services, and/or consulting experience.
• Understanding of traditional search engine metrics like precision, recall, Mean Reciprocal Rank (MRR), and Normalized Discounted Cumulative Gain (NDCG)
  
• Customer focused, strategic, drives for results, is self-motivated, and has a propensity for action.

Applied Sciences IC4 - The typical base pay range for this role across the U.S. is USD $117,200 - $229,200 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $153,600 - $250,200 per year.

Certain roles may be eligible for benefits and other compensation. Find additional benefits a