Quantitative Analyst – Cricket

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.

About the role

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, horseracingand golf.

In this role, you willjoin ourcurrentteam of cricketquant analystsindevelopingstatistical modelsprimarilyforcricket,while alsosupportingresearch into othersports,as well asinvestigatinghow our predictions can beleveragedto improve profitability and the overall commercial performance.

Furthermore,youwillplay 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 teammembersandyou willthereforebe able toallocatededicated 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.

Whilewe are open to applications from anyone who meets theminimumrequirements, we would be especially keen to hear from applicants with more substantial research experience for this particular role.

• Contribute toidentifyingpromising research directions; ensure research is carried out to the highest standard
• Developcricketpredictive models forboth pre-match andin-play
• Contribute to discussions and efforts toidentifyweaknesses and potential improvements in existing models across all sports
• Support Smartodds’ clients in their pricing tasks by developing,maintaining and supporting the mathematical libraries behind our range of tools and models and software that delivers model predictions into production
• Perform statistical analysis of datasets, testing well-defined hypotheses and effectively communicating results to the various stakeholders, including Smartodds’ clients
• Attend at least one event to support your professional development as a sports quant analyst on an annual basis: conferences, courses, meet-ups, networking events; in-person or remotely; on sports, statistics, machine learning, gambling, etc.

Required

• MSc in Statistics or a related field (e.g., Data Science or Mathematics), or another field (such as Computer Science, Engineering, Finance, etc) with some experience in statistics
• PhD or equivalent in statistics(orrelated area) or3+ years of work experience in a relevant role,e.gSports quantitative analyst for a betting syndicate or a bookmaker
• Extensive experience of probabilistic and statistical modelling
• Strong programming skills in one high level language such as R or Python
• Demonstrated passion for working in sports modelling,evidenced by personal projects, MSc project in a related area or statistical analyses of sports or teams
• Ability to communicate results to those with and without specialist knowledge
• Ability to work in the UK

Preferred

• A strong interest incricketdemonstratedbypreviousattempts to model outcomes or analyse data
• Good understanding of sports betting markets
• Experience with and/or knowledge of Bayesian models, state space models, filtering and smoothing, computational statistics and approximate inference methods
• Experience with and/or…