Do you want to be on the leading edge of using big data and help drive development decisions for the biggest productivity software suite on the planet? The Office Experience Organization (OXO) has embarked on a mission to delight our customers by using data informed engineering to develop compelling products and services.
OXO is looking for an experienced Data Scientist with a passion for maximizing the return on investment from collecting petabytes of telemetry data to infer deep user and product insights. The Data Scientist would leverage statistical modeling, experimentation, forecasting and data visualization techniques to identify core drivers of user engagement, satisfaction and consequent growth and revenue in Microsoft project and Microsoft planner, key applications in the productivity suite.
The role involves working with application teams (like Word, Excel, PowerPoint, Project, Planner, OneNote, Teams and Outlook), other data scientists, data engineers and program managers to continuously improve understanding usage, retention and user satisfaction.
We are looking for a strong Data Scientist with a proven track record of driving cultural changes needed to build maturity in the space of data driven decision making. Ideal candidates should be able to identify a business or engineering problem and translate it to a data science problem, dig out sources of data, conduct the analysis and modeling, including solving complex machine learning problems that would reveal useful nuggets. They should also be able to work with the PMs and the engineering teams to operationalize the solutions.
- Identifies data sources, integrates multiple sources, or types of data, and applies expertise within a data source to develop methods to compensate for limitations and extend the applicability of the data.
- Applies (or develops if necessary) tools and pipelines to efficiently collect, clean, and prepare massive volumes of data for analysis.
- Transforms formulated problems into implementation plans for experiments by applying (and creating when necessary) the appropriate methods, algorithms, and tools, and statistically validating the results against biases and errors.
- Interprets results and develops insights into formulated problems within the business/customer context and provides guidance on risks and limitations.
- Acquires and uses broad knowledge of innovative methods, algorithms, and tools from within Microsoft and from the scientific literature and applies his or her own analysis of scalability and applicability to the formulated problem.
- Validates, monitors, and drives continuous improvement to methods, and proposes enhancements to data sources that improve usability and results.
- Works collaboratively with PMs to translate the business needs into metrics and then works with data engineering to implement these metrics including identifying and obtaining the necessary data.
- Expert in one or more statistical software like R, SAS, etc.
- Expert in one or more scripting languages like Perl, Python, Scala, or SQL
- Solid foundation of statistical modeling and machine learning algorithms and experimental design
- Deep understanding of big data systems including map reduce technologies like Hadoop and Spark.
- B.S. and/or M.S. (Ph.D. Preferred) in Computer Science, Statistics, Operations Research, or similar quantitative field.
- 5 years plus of applying statistical modeling, ML, and data mining algorithms to real world problems.
Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, color, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran status, race, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable laws, regulations and ordinances.
Benefits/perks listed below may vary depending on the nature of your employment with Microsoft and the country where you work.