Job description

You will be a skilled, hands-on contributor to data science projects. You will be contributing as part of an established data science team which delivers in a variety of areas across the business. This is a predominantly hands-on role, although you will be interacting with business stakeholders and there will be opportunities to take increased responsibility for deliveries as your skills develop.
The Data Science team is part of a larger data and analytics group that numbers around 100 people. This wider group has separate functions for Data Analytics, BI, and Data Engineering as well as a team that supports Data Science with DevOps, Data Engineering and Developer resources. This structure allows the Data Science team to focus on the core data science work and solve some of the hardest problems in the business.
As an online high transaction business, Kindred Group collects a huge amount of data. This includes data about our customer's betting and game play activity, their interests and motivations and much more. We have invested heavily in data technologies and associated analytical tools that enables our data scientist to provide innovative solutions using the latest techniques and technologies.
Some of our project categories are personalisation and recommendations, customer propensity models, cybersecurity, risk management, anti-money-laundering (AML), entity resolution, payment/transaction fraud detection, and problem gambler detection.

Key Responsibilities
  • Hands on contributor, applying machine learning methodologies to delivering data science projects that allow the company to achieve its goals.
  • Perform data analysis and modelling on large (Tb) data sets.
  • Analyse wide range of data sources to identify new business value.
  • Support measurement initiatives to demonstrate efficacy of solutions to stakeholders.

Experience, Knowledge, & Skills
  • Circa 1-2 years commercial experience, ideally in a data science role
  • PhD or Masters in a numerate discipline.
  • Strong Python skills.
  • Solid understanding of statistical modelling and machine learning.
  • Excellent interpersonal skills and the ability to explain complex topics to stakeholders.
  • Ideally experience putting models and processes into production.
  • Ideally experience in cloud computing, in particular AWS.

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