Principal Applied Scientist

Microsoft•Redmond, WA

About The Position

The Scheduling service team is looking for a Principal Applied Scientist to provide technical leadership for the Scheduling Service, defining algorithmic strategy and driving delivery of a production-grade optimization platform. Microsoft products support a variety of customer scheduling needs, including meetings, project timelines, supply chain optimization, and frontline worker routing. The Scheduling Service team has been given ambitious goals of being the scheduling service of choice across all Microsoft products and an industry leader in this space. While the team has a broad agenda across multiple domains, today, our primary focus is on optimizing the scheduling and routing of Field Service workers to ensure efficient operations and improved customer satisfaction. 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.

Requirements

Bachelor'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 Master'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 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.
Ability to meet Microsoft, customer and/or government security screening requirements are required for this role.
Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud background check upon hire/transfer and every two years thereafter.

Nice To Haves

Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 8+ 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 6-12+ 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.
8+ years (or equivalent) in applied optimization/ML in production.
Experience in vehicle routing problems or scheduling optimization (algorithms).
Proven technical leadership (leading major initiatives, leading major initiatives).
Experience with coding in languages including, but not limited to, C, C++, C#.
Background in Multi-agent Planning and Scheduling and/or Operations Research.
Experience running your algorithms in a production environment and responding to customer inquiries.
Led development of optimization systems combining OR + ML
Experience in Genetic / heuristic scheduling algorithms, large neighborhood search and/or other optimization at scale.
Proven experience in solving vehicle routing problems.
Proven ability to lead problem framing and knowledge elicitation with customers and internal stakeholders, aligning on tradeoffs (cost/quality/latency/robustness) and driving measurable outcomes through production optimization systems.

Responsibilities

design and build innovative scheduling algorithms within our suite of scheduling solutions
set technical direction for scheduling/routing
define metrics and evaluation north-star
lead architecture decisions and drive cross-team execution, including collaborations with research organizations to bridge the gap between research and practice.

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