Data Science IC2

Microsoft’s Cloud business is expanding, and the Cloud Supply Chain (CSCP) organization is responsible for enabling the hardware infrastructure underlying this growth including AI! CSCP’s vision is to empower customers to achieve more by delivering Cloud and AI capabilities at scale. Our mission is to deliver the world's computer with an industry-leading supply chain. The CSCP organization is responsible for traditional supply chain functions such as plan, source, make, deliver, but also manages supportability (spares), sustainability, and decommissioning of datacenter assets worldwide. We deliver the core infrastructure and foundational technologies for Microsoft's over 200 online businesses including Bing, MSN, Office 365, Xbox Live, OneDrive and the Microsoft Azure platform for external customers. Our infrastructure is supported by more than 300 datacenters around the world that enable services for more than 1 billion customers in over 90 countries.
Microsoft Cloud Planning (MCP) is the central planning function within CSCP focused on forecasting, demand planning, and supply planning for all Microsoft Cloud services and associated hardware, directly impacting the success of Microsoft's cloud business. 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. #CSCP #CSCPJobs

Required/minimum qualifications
Other Requirements: Ability to meet Microsoft, customer and/or government security screening requirements are required for this role. These requirements include, but are not limited to, the following specialized security screenings:

Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud background check upon hire/transfer and every two years thereafter.

Preferred qualifications: PhD in Statistics, Applied Mathematics, Applied Economics, Computer Science or Engineering, Data Science, Operations Research or similar applied quantitative field. Experience in machine learning using R or Python (scikit / numpy / pandas / statsmodel) with skill level at or near fluency. Experience with deep learning models (e.g., tensorflow, PyTorch, CNTK) and solid knowledge of theory and practice. Prior experience in time series forecasting. Prior experience with typical data management systems and tools such as SQL. Knowledge and ability to work within a large-scale computing or big data context, and hands-on experience with Hadoop, Spark, DataBricks or similar. Practical and professional experience contributing to and maintaining a large code base with code versioning systems such as Git. Knowledge of supply chain models, operations research techniques, optimization modelling and solvers. Excellent analytical skills; ability to understand business needs and translate them into technical solutions, including analysis specifications and models. Creative thinking skills with emphasis on developing innovative methods to solve hard problems under ambiguity and no obvious solutions. Good interpersonal and communication (verbal and written) skills, including the ability to write concise and accurate technical documentation and communicate technical ideas to non-technical audiences.

Researches and develops production-grade models (forecasting, anomaly detection, optimization, clustering, etc.) for our global cloud business by using statistical and machine learning techniques. Manages large volumes of data, and creates new and improved solutions for data collection, management, analyses, and data science model development. Drives the onboarding of new data and the refinement of existing data sources through feature engineering and feature selection. Applies statistical concepts and cutting-edge machine learning techniques to analyze cloud demand and optimize our data science model code for distributed computing platforms and task automation. Works closely with other data scientists and data engineers to deploy models that drive cloud infrastructure capacity planning. Presents analytical findings and business insights to project managers, stakeholders, and senior leadership and keeps abreast of new statistical / machine learning techniques and implement them as appropriate to improve predictive performance. Oversees and directs the plan or forecast across the company for demand planning. Evangelizes the demand plan with other leaders. Drives clarity and understanding of what is required to achieve the plan (e.g., promotions, sales resources, collaborative planning, forecasting, and replenishment [CPFR], budget, engineering changes) and assesses plans to mitigate potential risks and issues. Oversees the analysis of data and leads the team in identifying trends, patterns, correlations, and insights to develop new forecasting models and improve existing models. Oversees development of short and long term demand forecasts and develops and publishes key forecast accuracy metrics. Analyzes data to identify potential sources of forecasting error. Serves as an expert resource and leader of demand planning across the company and ensures that business drivers are incorporated into the plan (e.g., forecast, budget). Leads collaboration among team and leverages data to identify pockets of opportunity to apply state-of-the-art algorithms to improve a solution to a business problem. Consistently leverages knowledge of techniques to optimize analysis using algorithms. Modifies statistical analysis tools for evaluating Machine Learning models. Solves deep and challenging problems for circumstances such as when model predictions are not correct, when models do not match the training data or the design outcomes when the data is not clean when it is unclear which analyses to run, and when the process is ambiguous. Provides coaching to team members on business context, interpretation, and the implications of findings. Interprets findings and their implications for multiple businesses, and champions methodological rigor by calling attention to the limitations of knowledge wherever biases in data, methods, and analysis exist. Generates and leverages insights that inform future studies and reframe the research agenda. Informs both current business decisions by implementing and adapting supply-chain strategies through complex business intelligence. Connects across functional teams and the broader organization outside of Demand Planning to advocate for continuous improvement and maintain best practices. Leads broad governance and rhythm of the business processes that ensure cross-group collaboration, discussion of key issues, and an opportunity to build proposed solutions to address current or future business needs.