|Job Type:||Full Time|
Microsoft Advertising (MSA) is a worldwide organization on the cutting edge of the digital advertising industry. We are the engine that powers the buying and selling of digital advertising across all aspects of our digital portfolio including our high-growth search engine, Bing, and consumer services like MSN, Microsoft News, Xbox, and Outlook.
Advertiser Market Sciences Team in MSA is hiring talented, highly motivated and productive individuals with expertise in the areas of: Computer Science, Machine Learning, Econometrics, Statistics, Modeling, Simulation and Data Mining. The team develops and applies advanced techniques to turn our petabytes of data into insights; and to drive actions based on those insights. The team works closely with partners across MSA to enable rigorous, effective, and data-driven decision making. Some example of the challenges we face:
- Modeling the dynamics of the paid search market
- Understanding Advertiser value, lifecycle, opportunity and marketing objectives
- Designing and analyzing the results of large-scale online experiments
- Prototyping algorithms fundamental to managing and optimizing demand generation activities to support our marketplace.
At Microsoft Advertising, we offer a strong team environment, exciting, applied research challenges, and a fun place to work. The work environment empowers you to have a real impact on Microsoft’s business, our advertiser partners, and millions of end users. This role is a unique opportunity to work with a world-class, interdisciplinary group of data scientists, analysts, sales and product managers.
Develop and manage analyses and algorithms that generate actionable insights and programs to improve Microsoft Ads demand generation activities including increasing both long-term revenue and relevance. Research and develop solutions for improving profits for Microsoft and returning value to the audience, advertisers and publishers (e.g. Ecosystem health, marketplace performance measurement, advertiser health, outlier detection, etc.). Specific responsibilities include the following:
- Work with key business stakeholders to understand the underlying business needs and formulate the needs into discrete, manageable problems with well-defined measurable objectives and outcomes.
- Identify and apply the appropriate methods/tools to efficiently collect, clean, and prepare massive volumes of data for analysis
- Transform formulated problems into implementations plans for experiments by defining success metrics, applying/creating the appropriate methods, algorithms, and tools, as well as delivering statistically valid and reliable results
- Develop new predictive and prescriptive models using advanced statistical and machine learning techniques with a goal of productionizing solutions
- Influence stakeholders to make product/service improvements that yield customer/business value by effectively making compelling cases through storytelling, visualizations, and other influencing tools
- Identify and analyze the applicability and scalability of innovative data science methods, algorithms, and tools from within Microsoft and from the scientific literature to create/test solutions that deliver value to customer and business
- A Master’s degree or equivalent experience in Data Science, Computer Science, Electrical Engineering, Mathematics, Machine Learning/AI or related fields.
- Demonstrated experience in all phases of managing data science projects including: problem definition, solution formulation, model building, productionizing and delivering measurable impact.
- Experience with online data; experience with online-advertising data strongly preferred.
- Knowledge and experience in at least three of the following areas: machine learning, data mining, user modeling, information retrieval (interrogation of log files and very large databases), economic modeling, econometrics, game theory, statistics, data analysis, e-metrics/measurement. 2+ years practical experience, 4+ years are preferred.
- Experience with data analysis and statistical tools (e.g. Python, R, SAS, Matlab or SPSS).
- Solid communications skills, both verbal and written.
- Hands-on approach to data analysis and a strong focus on quality.
- Ability to work independently and collaboratively in an interdisciplinary team environment.