Senior Applied Scientist
Microsoft
Senior Applied Scientist
Redmond, Washington, United States
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Overview
Do you have a keen interest in applying cutting-edge science to solve real-world problems at scale? Do you thrive in environments where you work with massive datasets and advanced machine learning techniques? Are you excited by the challenge of building intelligent systems that process trillions of records to deliver impactful experiences for millions of users?
At Microsoft AI, we are redefining what’s possible with data and AI. We’re seeking a Senior Applied Scientist to help design and develop the next generation of big data and AI-driven capabilities.
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.
Starting January 26, 2026, Microsoft AI (MAI) employees who live within a 50- mile commute of a designated Microsoft office in the U.S. or 25-mile commute of a non-U.S., country-specific location are expected to work from the office at least four days per week. This expectation is subject to local law and may vary by jurisdiction.
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.
- 4+ years of experience in Python/R/Scala or similar technology.
- 4+ years of experience in data science, analytics, or related areas.
- 3+ years of experience in applied machine learning and statistical modeling.
- 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 Qualifications:
- Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research)
- OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research)
- OR equivalent experience.
- 5+ years of experience in Python/R/Scala or similar technology.
- 5+ years of experience in data science, analytics, or related areas.
- Experience in structure un-scoped problems, define success metrics, and drive execution under uncertainty.
- Experience moving applied research into shipped product features
- Experience with SQL/R/Python/or similar to implement statistical models, machine learning, and analysis in big data environment.
- Experience in large scale computing systems like Spark, Hadoop, MapReduce and/or similar systems.
Applied Sciences IC4 - The typical base pay range for this role across the U.S. is USD $119,800 - $234,700 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 $158,400 - $258,000 per year.
Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here: https://careers.microsoft.com/us/en/us-corporate-pay
Microsoft will accept applications and processes offers for these roles on an ongoing basis.
#MicrosoftAI #BING
Responsibilities
- Apply advanced statistical modeling, machine learning, and analytics techniques to tackle complex problems such as fraud/anomaly detection, opportunities and business impact analytics.
- Design and analyze experiments to validate hypotheses and measure impact on products.
- Communicate findings and recommendations to technical and business stakeholders through clear, actionable insights.
- Drive projects from concept to production in a fast-paced, dynamic environment.
- Experiment, prototype, and evaluate new ideas.