Leveraging data and scientific models to benchmark how Microsoft products are performing relative to the competitive space and bring that "outside-in" perspective to inform senior executive decision making, especially in the AI landscape.
Requirements
- 3+ years of experience with SQL, R, Python to implement statistical models, machine learning, and analysis (Recommenders, Prediction, Classification, Clustering, etc.) in big data environment.
- Experience taking sample data and scaling it up to create an estimate of the population.
- Experience benchmarking between metrics to calculate relative performance or share.
- Experience exploring to find new data and leveraging that to stand up new metrics.
- 3+ year of experience in delivering on ambiguous projects with incomplete or imperfect data
Responsibilities
- Understand problems facing projects and is able to leverage knowledge of data science to be able to uncover important factors that can influence outcomes on specific products.
- Develop useable data sets for modeling purposes.
- Write efficient, readable, extensible code from scratch that spans multiple features/solutions.
- Apply a customer-oriented focus by understanding customer needs and perspectives, validating customer perspectives, and focusing on broader customer organization/context.
- Build trust with customers by leveraging interpretability and knowledge of Microsoft products and solutions.
- Leverage capabilities within existing systems.
- Acquires data necessary for successful completion of the project plan.
Other
- 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.
- 3+ years of experience with written and verbal communication to educate and work with cross functional teams.
- Coach less experienced engineers in standards and best practices.
- Use their understanding of organizational dynamics, interrelationships among teams, schedule constraints, and resource constraints to effectively influence partners to take action on insights.
