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Applied Scientist

Boston, MA

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As a senior applied scientist, you will invent, build and deploy state of the art machine-learning models and systems to enable and enhance the team's mission. You will develop a long-term scientific agenda, initiate and lead scientific projects and mentor applied scientists. You will present your work to product and engineering teams, publish scientific papers and apply for patents for your inventions. You come with experience defining research vision and getting buy-in from senior research and business leader across the company.

Responsibilities: Advance exploratory research projects in machine learning and related fields to create highly innovative customer experiences
Analyze large amounts of data to discover patterns, find opportunities, and develop highly innovative, scalable algorithms to seize these opportunities
Validate models via statistically rigorous experiments across millions of customers;
Work closely with software engineering teams to build scalable prototypes for testing, and integrate successful models and algorithms in production systems at very large scale
Technically lead and mentor scientists
Qualifications: Ph.D degree in Computer Science, Mathematics, Statistics, Physics or another quantitative field
Strong technical credentials, with at least 5 years of professional or post-doctoral experience working in a relevant field: machine learning, deep learning, knowledge bases, recommendation systems, information retrieval, natural language understanding, robotics, statistics, computer vision etc.
Significant peer-reviewed scientific contributions in premier journals and conferences.
Solid fundamentals in problem solving, algorithm design, complexity analysis, mathematics and statistics.
Proficiency in a major programming language (Python, Java, Scala or similar).
Proven track record in leading scientific projects and mentoring scientists;
Superior verbal and written communication and presentation skills, ability to convey rigorous mathematical concepts and considerations to non-experts.
Practical experience with big-data processing libraries, eg. Apache Spark, Apache Beam, Apache Pig, Hadoop or similar preferred
Practical experience with building and evaluating deep-learning models using major libraries eg: mxNet, TensorFlow, Keras or similar preferred
Proven track record of production achievements, handling gigabyte and terabyte-size datasets

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