Data & Applied Scientist at Microsoft

Data & Applied Scientist Details

Jan. 17, 2019, 3:51 a.m.
Engineering
Individual Contributor
Full-Time
Redmond, WA
Analytics (CGA) team Azure Customer Growth
Microsoft's Cloud and AI group has a unique opportunity at the intersection of cloud computing and artificial intelligence. Fueled by Microsoft's cloud-first vision, we are disrupting the cloud industry with innovation and large-scale AI implementation. The goal of the Azure Customer Growth and Analytics (CGA) team is to foster a data-driven culture; to encourage and enable the entire organization to make more informed decisions through data. In support of this mission, our data science team carries out applied research in ML/AI and designs, develops and deploys state of the art algorithms for various business scenarios. Some of the more recent developments include applications of deep learning, neural networks, boosted decision trees, sparse linear models, and customer segmentation, which have been executed in partnership with teams across engineering, finance, business planning, and sales and marketing. Our team also has a strong presence in both internal and external data science and

RESPONSIBILITIES: Develop new predictive and prescriptive models using advanced research techniques with a goal of productionalized solutions. Collaborate closely with Analytics, Engineering, and Experimentation teams by demonstrating cross-functional resource interaction to deliver ML models. Identify and investigate new technologies, prototype and test solutions for product features, and design and validate designs that deliver an exceptional user experience. Combine broad and deep knowledge of relevant research domains with the ability to synthesize a wide range of requirements to make significant
BASIC QUALIFICATIONS: PhD in Computer science, Operations Research, Statistics, Applied Mathematics, Electrical Engineering, or Physics with strong knowledge of machine learning. 4+ years of industry experience in handling high volumes of structured and unstructured data, with a proven track record of leveraging data science practices to drive significant business impact. Quantitative methods should span Deep Learning, statistical modeling, machine learning, optimization methods, econometrics, graph theories and NLP. Adapt ML and neural network algorithms and architectures to best exploit modern
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