Microsoft Data Applied Scientist II 

India 

936022132

* M.Sc. in Statistics, Applied Mathematics, Applied Economics, Computer Science or Engineering, Data Science, Operations Research or similar applied quantitative field
* 4-8 years of industry experience in developing production-grade statistical and machine learning code in a collaborative team environment.
* Prior experience in machine learning using R or Python (scikit / numpy / pandas / statsmodel).
* 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.
* 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.

* 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.
* 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.

Responsibilities:

* Researching and developing production-grade models (forecasting, anomaly detection, optimization, clustering, etc.) for our global cloud business by using statistical and machine learning techniques.
* Manage large volumes of data, and create new and improved solutions for data collection, management, analyses, and data science model development.
* Drive the onboarding of new data and the refinement of existing data sources through feature engineering and feature selection.
* Apply 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.
* Work closely with other data scientists and data engineers to deploy models that drive cloud infrastructure capacity planning.
* Present analytical findings and business insights to project managers, stakeholders, and senior leadership and keep 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 (e.g., weekly, monthly, quarterly) 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).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.* 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.