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A sports analytics company in the City of London seeks a Cricket Quantitative Analyst to join its Quant Team. The role involves developing statistical models primarily for cricket, supporting various sports research, and delivering high-quality predictions. Candidates should possess an MSc in Statistics or a related field and extensive modelling experience, preferably with a passion for sports analysis. A collaborative culture is emphasized, along with professional development opportunities and a comprehensive benefits package.
We have a fantastic new opportunity to join our team at Smartodds as a Cricket Quantitative Analyst. Based in North London, Smartodds provides in-depth research and analysis on sporting events around the world, supported by world-class, bespoke software platforms. We are proud of our collaborative and dynamic culture, grounded in our core values of Boldness, Open-mindedness, Ownership, and Togetherness. We are a supportive and collaborative team - our environment is open, inclusive, and focused on doing great work together.
As a member of the Quant Team, you will join an exciting environment, predicting outcomes of professional sports on behalf of our clients. We focus on football, baseball, basketball, cricket, tennis, American football, ice hockey, horseracing and golf.
In this role, you will join our current team of cricket quant analysts developing statistical models primarily for cricket, while also supporting research into other sports, as well as investigating how our predictions can be leveraged to improve profitability and the overall commercial performance.
Furthermore, you will play a key role in developing and supporting the reliable production of high-quality predictions for our clients. We highly value the personal development of our team members and you will therefore be allotted dedicated time to improve your skills and gain the necessary experience that will enable you to progress into more senior roles.
You’ll have plenty of autonomy to execute your models from idea to code to validation to (hopefully) deployment, integrating your well documented and tested code into our internal libraries to help in prediction for at least one of the above sports.
The atmosphere is a collaborative academic one with peer reviews, research talks, and the opportunity for further education. Unlike academia though, the market is there to give immediate feedback on how good your model is. This makes the job challenging but also very exciting.
While we are open to applications from anyone who meets the minimum requirements, we would be especially keen to hear from applicants with more substantial research experience for this particular role.
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