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 & 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 AI/ML conferences.
We are looking for a passionate, talented, and innovative Data Scientist with a strong machine learning and deep learning background to help build industry-leading solutions. As a senior data scientist in the Azure CGA team, you'll collaborate with partner teams and other researchers at Microsoft to build new ML solutions to solve critical and complex business problems. As part of this process, and with support from our data platform and engineering team, you will be working with huge volumes of data to solve real-world data science problems.
If you are seeking an iterative, fast-paced environment where you can drive innovation, apply state-of-the-art technologies to solve extreme-scale real world delivery challenges, and have business impact on global scale, this is your opportunity. Join us and be a part of an exciting team that is shaping the evolution of cloud computing
* 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 parallel environments (e.g. distributed clusters, multicore SMP and GPU).
* 2+ years of experience in applying deep learning models.
* Strong skills in at least 3 languages such as Python, R, SQL, Java or Scala.
* Experience with open source tools like CNTK, Tensor flow, MxNet, Caffee and OpenCV and Big data technologies like Hive, PySpark, SparkR, Databricks etc.
* Outstanding research track record in related areas, with evidence through academic publications and services
* Strong theory/algorithmic background and good understanding on how to apply advanced knowledge to solve real problems
Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, color, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran status, race, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable laws, regulations and ordinances.
Benefits/perks listed below may vary depending on the nature of your employment with Microsoft and the country where you work.
* 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 contributions to the feature roadmap for the applied machine learning platform.
* Take responsibility for technical problem solving, including creatively meeting product objectives and developing best practices.
* Own strategic thought leadership for the subject of enterprise-wide machine learning capabilities
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