BetBuddy, part of Playtech Plc (PTEC:LSE), is a regtech solution provider that helps lotteries and commercial gaming operators to understand consumer behaviour and manage risk. We developed BetBuddy to solve major issues facing the gaming industry - mainly how can gaming operators predict those players at risk of harm, and how can they interact with these customers to help them to enjoy their experiences and retain them as long term customers. BetBuddy has continued to build important commercial relationships and continued to deploy its products and services to help operators to meet increasingly stringent regulatory requirements, whilst at the same creating value from compliance.
BetBuddy was recently acquired by Playtech, the world's largest iGaming software supplier, who work with the leading operators in the iGaming business e.g., Bet365, LadbokesCoral Group, SkyBet, etc.
BetBuddy’s customers cover both the land and online sectors. BetBuddy made large strides in product innovation and development in applying AI to delivering faster, more accurate, and more efficient compliance. Product innovation has been undertaken in partnership with the University of London’s Research Centre for Machine Learning. This included showcasing new research in Explainable AI alongside Google DeepMind and Facebook AI research at the world leading AI conference, NIPS. R&D in emerging technology areas like AI bring tremendous benefits to industry.
Larger gaming operators struggle to innovate quickly and BetBuddy is ultimately helping the industry to de-risk and adopt emerging technologies. At the same time BetBuddy has remained at the forefront of Responsible Gambling (RG) research, publishing in the world’s leading peer-reviewed journals and contributing thought leadership at the major RG conferences. BetBuddy is helping the industry tackle new compliance requirements on the front foot to enable the industry to grow sustainably.
- Working with large data sets; clean, structure and analyse data
- Build data models using various machine learning techniques
- Work with the engineering team in building machine learning applications capable of managing large data sets
- Work within the data science teams to develop repeatable and robust data science infrastructure and processes
- Oversee post-graduate student's research project(s) in the fields of AI.
- A 1st or 2:1 with a degree in Computer Science, Maths, Physics, or similar, preferable technical Ph.D or Master degree also
- Excellent analytical and problem solving skills
- Good programming/coding skills
- Good technical hacking skills
- Fluent in English
- Data driven thinker
- Desire to work in agile environment
- Strong team player.
Knowledge/Experience with all of the following
- 3+ years’ experience in industrial data engineering i.e., programming/managing with large data sets, understanding of data structures and architectures
- Good understanding and work experience in Python and its packages like pandas, scikit-learn, matplotlib, etc
- Broad experience and exposure to supervised and unsupervised machine learning techniques.
- Preferable experience in working with Spark, Cassandra
Knowledge/Experience with all of the following would be a nice to have/bonus
- Deep learning / RNN / reinforcement learning
- Research methods.
Playtech is a leading software provider for online gaming operators and can be positively considered as one of the pioneers of the worldwide online and land-based gaming industry. Company's business portfolio consists of the most prominent names in the business, including Bet365, William Hill, PaddyPower, Gala, Coral, Betclic, Winner, Betfair, Poker770, etc. Behind the eminent success of Playtech's products and services there are around 5,000 people located in 14 countries, the majority of whom are engaged in research and development of current and future gaming technologies. For additional information on Playtech, and the Playtech Group of companies, please visit http://www.playtech.com
- Data Engineering
- Python + (pandas, scikit-learn, matplotlib)
- Spark, Cassandra
- Machine Learning Techniques
- Deep learning / RNN
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