February 27, 2017

Virtual Data Science Resourcing

Many companies face a data science resourcing deficit

  • Data scientists work best in teams
  • This allows them to mentor each other
  • And discuss alternative approaches to solving problems
  • While senior members can quality assure juniors’ work
  • And the team provides continuity when an individual leaves
  • But only the largest organisations can afford one

Our solution is a virtual data science team

  • You contract into a fixed level of data science resource
  • This is measured in days per week, from one to three
  • Except for holidays, sickness, and resignations you will always have the same person
  • Your data scientist is part of the actual Berry Thompson team
  • Supported in terms of training, mentoring, hardware and software
  • And in addition each client has their own business consultant

The role of the business consultant

  • The purpose of data science is to help you make better decisions
    • How to treat individual customers
    • How to optimise your marketing spend
  • The business consultant ensures that each project brief is aligned with your business needs, and the decisions you expect to make
  • He also manages the delivery of each project via your contracted data scientist
    • Advising on the most appropriate methodologies
    • Quality assuring all outputs
    • Providing the interpretation layer beyond the actual numbers
    • Spotting opportunities for further data science developments

What options do we provide?

Why Berry Thompson?

•Deep experience of delivering data science projects
•Currently working for blue chips including Swiss Re, Capco, Global Radio, Samsung, and many smaller concerns
•Supported by Dr Brian Birkhead as principal business consultant

We have enjoyed a very fruitful relationship with Julian & the team over the past 12 years. In a world where so many analytics companies just scratch the surface of the business issue and just “get down to the data”, what sets this team apart is its desire to truly understand their clients’ business (in our case, Life Insurance). This means that they better understand the scope and limitations of the data before performing the analysis – and this leads to better outcomes. William Trump, Swiss Re

 

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