Data Marketing Scientist

Company:  BettingJobs
Location: London
Closing Date: 02/11/2024
Hours: Full Time
Type: Permanent
Job Requirements / Description
BettingJobs are working with a sports analytics company who work with clients in the iGaming industry, who are seeking the hire of a Data Scientist to be based out of London. They are looking for a data scientist to work on the betting and prediction side of their business, helping them to improve their ability to predict match outcomes. You will have flexibility to work from home one day a week, and they aren't too fussy about the exact hours you work each day. But please note they are not considering remote candidates at the moment. An ability to make pragmatic and sensible choices about what data analysis and modelling approaches to use – you can judge when it’s right to obsess about the details, and when it’s right to ship an MVP as fast as possible. A flair for communicating statistical analyses to both technical and non-technical audiences Experience with statistical analysis and predictive modelling - you don’t need to be an expert in any one area, but you need to know what techniques and tools are available, and understand the trade-offs of using different approaches Experience with SQL and relational databases – you can quickly understand a new dataset, perform exploratory analysis, identify data quality issues, and take pragmatic decisions about how to handle imperfect data Some programming experience, preferably with Python. More experienced candidates will have the opportunity to take on more responsibility, leading projects and helping set the direction of the research. 3+ years of practical data science experience ~ Project management skills, including an ability to make sensible value judgements about where the team should spend time ~ An extensive knowledge of statistical methods and machine learning techniques ~ Strong programming skills, ideally with Python ~ Experience working with other data scientists on a collaborative codebase ~ Experience of deploying models to production, ideally in AWS or another cloud environment
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