Data Scientist - Advanced Analytics

Qualifications

  • Undergraduate degree in Maths, Statistics, Physics, or Engineering
  • 2 - 4 years of experience in programming; experience applied to business problems is a plus
  • Programming (focus on Machine Learning): R and/or Python (must), SPSS, SAS, Ruby, Hadoop (valued)
  • Data treatment/Data mining: SQL, AWK, Access, Spark, Excel (valued)
  • Statistical knowledge is a plus
  • Demonstrated aptitude for analytics
  • Proven record of leadership in a work setting and/or through extracurricular activities
  • Ability to work effectively with people at all levels in an organization and collaboratively in a team environment
  • Ability to communicate complex ideas effectively – both verbally and in writing – in English and French

Who You'll Work WithYou will be based in Paris and will be part of the Advanced Analytics team.

What You'll Do

You will focus on the development of Advanced Analytics models to optimize underlying business problems.

As a Data Scientist, you will work through our clients' entire data ecosystem to understand what's available, what's missing - and where to find and collect that information. You will develop, implement and maintain effective programs around all elements of data engineering and modeling with a constant eye toward continuous improvement.

In this role, you will shape the future of what data-driven organizations look like, drive processes for extracting and using that data in creative ways, and create new lines of thinking within an infinite number of clients and situations.

You will create valuable, transformative business strategies through the measurement, manipulation, reporting and dissemination of broad sets of data. You will apply and advise on state-of-the-art advanced analytic and quantitative tools and modeling techniques in order to derive business insights, solve complex business problems and improve decisions. You will review, support and advise on the day-to-day analytics requirements of clients' key operational processes while continually improving the impact of these processes.


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