Principal Data Science Lead at Microsoft

Principal Data Science Lead Details

Feb. 13, 2019, 12:40 a.m.
People Manager
Redmond, WA
Edge [COSINE] team Microsoft Cloud solutions
Microsoft envisions a world where passionate innovators come to collaborate, envisioning what can be and taking their careers places they simply couldn't anywhere else. This is a world of more possibility, more innovation, more openness, and sky's-the-limit thinking - a cloud-enabled world. Our mission is to empower every person and every organization on the planet to achieve more. This mission is bold and at the core of what our customers and employees care deeply about. We have unique capability in harmonizing the needs of both individuals and organizations. We deeply care about taking our ideals and vision global and making a difference in lives and organizations in all corners of the planet. We are always learning. Insatiably curious. We lean into uncertainty, take risks, and learn quickly from our mistakes. We build on each other's ideas, because we are better together. We stand in awe of what humans dare to achieve and are motivated every day to empower others to do more and achieve

Key responsibilities of the Principal Data Science Lead include: Own, generate and execute plans for ML/AI projects Build machine learning models using tools such as Python, R and Azure services Provide AI expertise for various practice groups in COSINE Work with Project Managers, Data, feature and ML engineers to design your projects. As a mentor, help individuals in the team grow their technical skills. Prepare technical papers and presentations, and publish them internally and externally
Basic Qualifications: Bachelor's Degree in data science, Statistics, Computer Science, Engineering or equivalent experience 2+ years of quantitative analytical work using Bayesian and frequentists statistics Experience in building products with machine learning techniques such as neural networks, clustering, random forests, etc. Fluent in R and Python 1+ year of experience in highly distributed large data environments Preferred Qualifications: Demonstrated understanding of the principles of research design and experiment design / analysis A self-starter who can work independently, pro-actively
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