Job Details
Are you a data scientist who wants to shape the future of Azure? Do you have a passion for solving complex business problems with data-driven solutions? Do you want to join a team that values diversity, inclusion, collaboration and innovation? If so, we have an exciting opportunity for you!
We are the Revenue Optimization and Applied Data Science (ROADS) team within the Azure Monetization & Business Planning organization. We are responsible for leading revenue efficiency efforts for one of the most important and significant areas of Microsoft. We use Azure cloud services and tools to define and execute our revenue optimization strategy pillars and drive the supporting data strategy. We also work closely with other data scientists, business planners, economists, marketing, and executives to align our revenue optimization and data initiatives with our business goals and vision.
As a Senior Data Scientist on our team, you will have the opportunity to:
Create, build and bring your own ideas to market using your insights from data analysisLeverage your analytics and coding superpowers to create new pricing and revenue optimization features, offers and concepts for customersHave a meaningful impact on Azure s revenue growth and customer satisfactionDevelop innovative solutions for pricing and revenue optimization using AI and machine learning techniquesLearn from and collaborate with some of the best data scientists in the industryGrow your own experience and career in a supportive and respectful environment
Responsibilities
As a Senior Data Scientist for yield management, you will be responsible for:
Designing, implementing, and maintaining data pipelines using Azure Data Factory, Azure Databricks, Azure Synapse Analytics, etc.Performing exploratory data analysis using Azure Machine Learning Studio, Azure Data Lake Analytics, etc.Building predictive models using Azure Machine Learning Service, Azure Cognitive Services, etc.Evaluating model performance using appropriate metrics and techniquesDeploying models to production using Azure DevOps, Azure Kubernetes Service, etc.Generating insights and reports using Power BI, SQL Server Reporting Services, etc.Identifying opportunities for monetization and revenue optimization using data mining techniquesConducting A/B testing and experimentation to measure the impact of different pricing strategiesCommunicating complex data findings in a clear and concise manner to technical and non-technical audiencesCreating and building data-driven solutions for yield management using Azure cloud services and toolsAnalyzing data and generating insights to create new pricing and revenue optimization features, offers and concepts for customersHaving a meaningful impact on Azure s revenue growth and customer satisfaction
Qualifications
Required/Minimum Qualifications
Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 1+ year(s) data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results)OR Master's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 3+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results)OR Bachelor's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 5+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results)OR equivalent experience.5+ years experience in Python/R/SQL or other programming languages for data analysis
Additional Or Preferred Qualifications
Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 3+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results)OR Master's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 5+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results)OR Bachelor's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 7+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results)OR equivalent experience.Familiar with Azure cloud services and tools for data scienceExperience with machine learning frameworksExperience with web analytics and marketing analyticsExperience with monetization/revenue optimization techniques such as pricing optimization/demand forecasting/recommendation systems etc.
Data Science IC4 - The typical base pay range for this role across the U.S. is USD $112,000 - $218,400 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 $145,800 - $238,600 per year.
Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:
Microsoft is an equal opportunity employer. Consistent with applicable law, all qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.#J-18808-Ljbffr