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Data Scientist

 

Monetate is looking for a data scientist to be responsible for research and development for products whose value arises from statistical/machine learning models.

At Monetate, data science is central to our product strategy; the data scientist will have a key role in shaping the future of Monetate’s products and working cross-functionally to bring these products to market.

Monetate is a unique place to work on statistical and machine learning problems. As the global leader in multi-channel testing and personalization, Monetate influences more than a third of peak U.S. ecommerce sales. This global footprint results in data from over a billion consumer interactions every day, providing rich raw material for analysis and modeling. Monetate’s customers rely on the platform to take direct action in delivering better experiences to consumers, giving data scientists the opportunity to experimentally evaluate their models and techniques.

Essential Duties and Responsibilities:

  • Contribute to and enable execution of Monetate’s product roadmap by developing models to solve our customers’ pressing problems
  • Collaborate with and learn from a like-minded group of data scientists.  Share your expertise with others and learn from their expertise.
  • Develop models with a variety of requirements (online learning, supervised learning, unsupervised learning, large data sets, etc.)
  • Understand and apply cutting-edge statistical and machine learning techniques with novel applications to e-commerce and marketing problems.
  • Implement cutting-edge techniques for which off-the-shelf software is not available.
  • Assist product management and product engineering in building full-featured products based on your research.

Desired Skills:

  • Proficient in a modern data analysis stack (at least one of Python, R, Matlab, SAS, SPSS, STATA, etc.).
  • Proficient in SQL.
  • Ability to quickly become effective working in Python (experience in Python is desirable).
  • Ability to effectively communicate the results and value of models with technical stakeholders specializing in other areas.

Qualifications:

  • Fundamental grounding, academic or practical, in the foundations of math and computer science.
  • Postgraduate coursework or equivalent practical work (1+ years) in one or more of statistics, machine learning, math, computer science, econometrics, or a quantitative social science.
  • Practical understanding of how business value, applications, and infrastructure constraints drive modeling decisions.
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