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- Tasks: Develop and implement innovative sports models using advanced statistical methods.
- Company: Join a dynamic betting syndicate backed by an established firm in London.
- Benefits: Enjoy hybrid working options and competitive compensation with flexibility for top talent.
- Why this job: Shape the future of sports analytics in a creative, start-up culture within a reputable company.
- Qualifications: Strong academic background in maths or stats; experience in sports modelling is a plus.
- Other info: Be part of a growing team aiming for major industry shifts, including Asian market expansion.
The predicted salary is between 43200 - 72000 £ per year.
Betting syndicate build out from an established firm in London.
Compensation: Competitive, with flexibility for exceptional domain expertise.
Hybrid working - potential to be remote outside of the UK.
I'm looking for a quant analyst to join this growing quant & data science team with a strong background in mathematical modeling and statistics, ideally with expertise in sports models. This role offers the freedom to build models from the ground up, shaping the direction of this firm's sports analytics.
What You’ll Be Working On:
- Developing fundamental models for sports, for example: American football, baseball, tennis, and ice hockey.
- Pricing same-game parlays with a focus on rigorous statistical methods.
- Applying Bayesian inference, filtering techniques, and advanced mathematical stats.
- Collaborating with quant engineering teams to build dashboards and implement models.
- Driving innovation within a startup-style environment backed by an established company.
- Helping shape proprietary betting strategies, bet builders, and sportsbook feeds.
Who We’re Looking For:
- Strong academic background (Bachelor’s/Master’s; PhD is a plus but not required).
- Strong years of experience in a reputable syndicate or quantitative team.
- Deep domain knowledge in sports (American football/baseball is a bonus!).
- Experience building models from first principles, rather than tweaking existing frameworks.
- Proficiency in R, Python, or other relevant programming languages.
Why Join Us?
- Creative autonomy – take ownership of your sport and drive innovation.
- Start-up culture within an established company – flexibility to explore cutting-edge strategies.
- A growing quant team with long-term ambitions (20-25 people planned).
- Opportunity to be involved in major industry shifts, including expansion into regulated Asian markets.
This is a high-impact role where the right candidate will shape the direction of their sports models. Interested? Get in touch.
Sports quant analyst employer: Harrington Starr
Contact Detail:
Harrington Starr Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Sports quant analyst
✨Tip Number 1
Network with professionals in the sports analytics field. Attend industry conferences, webinars, or local meetups to connect with others who work in quant analysis and sports modelling. This can help you gain insights into the latest trends and potentially lead to referrals.
✨Tip Number 2
Showcase your expertise in sports models by engaging in relevant projects or competitions. Participate in hackathons or online challenges that focus on sports analytics, as this will not only enhance your skills but also provide tangible examples of your work to discuss during interviews.
✨Tip Number 3
Familiarise yourself with the specific sports mentioned in the job description, such as American football and baseball. Understanding the nuances of these sports will allow you to speak more knowledgeably about model development and demonstrate your passion for the field.
✨Tip Number 4
Prepare to discuss your experience with Bayesian inference and advanced statistical methods in detail. Be ready to explain how you've applied these techniques in past projects, as this will show your depth of knowledge and ability to contribute to the team's innovative approach.
We think you need these skills to ace Sports quant analyst
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your strong academic background and relevant experience in quantitative analysis, particularly in sports. Emphasise any specific projects or models you've developed that align with the job description.
Craft a Compelling Cover Letter: In your cover letter, express your passion for sports analytics and detail how your skills in mathematical modelling and programming languages like R or Python make you a perfect fit for the role. Mention your understanding of the betting industry and any innovative ideas you have.
Showcase Relevant Projects: If you have worked on any projects related to sports modelling or statistical analysis, include them in your application. Provide brief descriptions of the methodologies used and the outcomes achieved to demonstrate your expertise.
Highlight Collaboration Skills: Since the role involves collaborating with quant engineering teams, mention any previous experiences where you successfully worked in a team environment. Highlight your ability to communicate complex ideas clearly and effectively.
How to prepare for a job interview at Harrington Starr
✨Showcase Your Mathematical Modelling Skills
Be prepared to discuss your experience with mathematical modelling and statistics. Bring examples of models you've built, especially in sports analytics, and be ready to explain your thought process and the methodologies you used.
✨Demonstrate Domain Knowledge
Familiarise yourself with the specific sports mentioned in the job description, such as American football and baseball. Being able to discuss recent trends or statistics in these sports will show your passion and understanding of the domain.
✨Highlight Programming Proficiency
Make sure to mention your proficiency in programming languages like R and Python. Be ready to discuss any projects where you've applied these skills, particularly in building models or dashboards.
✨Emphasise Innovation and Creativity
This role values creative autonomy, so think of examples where you've driven innovation in your previous roles. Discuss how you approached problem-solving and any unique strategies you implemented in your work.
