The EY NextWave Data Science Challenge 2019 (Paid Internship & Fully Funded to EY’s headquarters in New York City)

The EY NextWave Data Science Challenge is a global competition for students interested in data science who want to make a difference. EY is looking for individuals to analyze real-world problems through data and make findings that will help build a better working world.

By participating in the EY NextWave Data Science Challenge, you will get to hone your technical skills, develop new expertise and, if you do well, win an internship or prize money and gain public recognition for your achievements. You will also be helping to build a better working world. Are you up for the challenge?

Eligibility

This competition is open to teams of up to two students who are
currently enrolled in a full-time or part-time academic program that
resides in one of the participant countries/regions:

  • Australia
  • Belgium
  • Brazil
  • China
  • France
  • Hong Kong
  • Indonesia
  • Ireland
  • Malta
  • Poland
  • Singapore
  • South Africa
  • Spain
  • United Kingdom
  • United States

Benefits:

EY is offering the winner/s in each country/region a paid internship with EY’s Data Analytics team*. In addition, the top three global finalists will receive a monetary reward:

  • First place: US$8,000
  • Second place: US$5,000
  • Third place: US$2,000

The challenge:

  • The challenge topic can be found in the platform, and it is connected to building a better working world.
  • On 1 April, we will publish all challenge details, including a dataset containing large amounts of information.
  • Your
    task will be to model historical data using your knowledge of data
    science, advanced analytics tools and techniques while basing your
    observations on the data provided. We encourage you to use externally
    available data to support your predictions and models.

Competing locally and globally

Participants
will compete at both a country/region and a global level. Dynamic
rankings will reflect the accuracy of your predictions. Your results
will be compared to those of other participants, both within your
country/region and worldwide.

Presenting your work

Once
the competition closes, the top performers in each country/region (there
will be a minimum of three) will be invited to present their findings
to the local EY leadership team. They will be asked to give a
presentation, which should cover:

  • Methodology and understanding of the problem, including algorithms used and data sources
  • Findings and patterns
  • Opportunities to improve individual and team performance
  • Proposed applications for the data findings within sport and / or health

Country/region winners

The
local EY leadership team will rate these presentations to assess the
depth of your analysis, the quality of your insights and your ability to
communicate your ideas, among other skills. The participant or team
with the highest score will be the winner of the country competition.
See country awards below.

Global winners

The three country/region winners with the highest rankings in the challenge will be invited to EY’s headquarters in New York City to meet with the firm’s executive leadership team.

Timeline

  • The competition platform is already open for registration: Click here to access
  • The challenge topic will be published here on 1 March, but won’t be available for enrolment until 1 April
  • The challenge will begin on 1 April
  • The challenge will close on 10 May
  • Final rankings will be published right after closure
  • As
    of 11 May, top performers in each country/region (at least three,
    depending on the country) will be invited to present their work in an
    award ceremony that is hosted by the local EY leadership team. This
    event will take place in May, with the date being set by each
    country/region on an individual basis.
  • The global award ceremony for the three global winners of the challenge will be in New York City, USA on June 14.

For More Information:

Visit the Official Webpage of the 2019EY NextWave Data Science Challenge

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