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- Tasks: Lead the design and deployment of ML models for sports predictions.
- Company: Join a cutting-edge sports betting company with a remote-first culture.
- Benefits: Generous compensation, remote work, and opportunities for professional growth.
- Why this job: Make an impact in sports betting with innovative machine learning solutions.
- Qualifications: 5+ years in data science, leadership experience, and sports betting model expertise.
- Other info: Collaborative environment with a focus on continuous learning and development.
The predicted salary is between 54000 - 84000 £ per year.
We're looking for a Lead Data Scientist to head our machine learning model development operation. You'll design, build, and deploy predictive models that estimate sports outcome probabilities, models that directly drive our betting strategy on prediction markets. This is a hands-on leadership role. You'll own the full model lifecycle from research through production, lead a team of data scientists, and continuously improve our predictive edge. The role will be fully remote with a generous compensation package; previous experience with building sports betting models is essential.
Responsibilities
- Lead the design, development, and deployment of machine learning models for sports outcome prediction
- Manage and mentor a team of data scientists and data engineers
- Build and validate deep learning architectures (CNNs, Transformers, neural networks) for structured sports data
- Develop back testing frameworks and rigorously validate model performance against historical data
- Collaborate with trading and engineering teams to integrate models into live betting operations
- Stay current with advances in sports analytics, ML research, and betting market dynamics
Requirements
- 5+ years of experience as a Data Scientist or ML Engineer, with at least 2 years in a leadership role
- Proven experience building sports betting or prediction models — this is essential
- Strong expertise in deep learning frameworks (PyTorch, TensorFlow) and techniques (neural networks, CNNs, Transformers)
- Advanced proficiency in Python and SQL
- Solid foundation in statistics, probability theory, and predictive modelling
- Experience deploying ML models to production environments
- Excellent communication skills — ability to translate complex findings for non-technical stakeholders
- Degree in Computer Science, Data Science, Statistics, Mathematics, Physics, or related quantitative field
Lead Data Scientist- Sports Betting ML in London employer: SIRE
Contact Detail:
SIRE Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Lead Data Scientist- Sports Betting ML in London
✨Tip Number 1
Network like a pro! Reach out to your connections in the sports betting and data science fields. Attend industry meetups or webinars, and don’t be shy about asking for introductions. We all know that sometimes it’s not just what you know, but who you know!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your previous work with predictive models, especially those related to sports betting. We recommend including case studies or projects that highlight your expertise in deep learning frameworks. This will make you stand out when applying through our website.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and soft skills. We suggest practising common interview questions related to machine learning and leadership. Don’t forget to have examples ready that demonstrate your experience in building sports betting models!
✨Tip Number 4
Follow up after interviews! A quick thank-you email can go a long way. We believe it shows your enthusiasm for the role and keeps you fresh in the interviewer's mind. Plus, it’s a great opportunity to reiterate why you’re the perfect fit for the Lead Data Scientist position.
We think you need these skills to ace Lead Data Scientist- Sports Betting ML in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience in building sports betting models and leading teams. We want to see how your skills align with the role, so don’t be shy about showcasing your relevant projects!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re passionate about sports analytics and how your background makes you the perfect fit for our Lead Data Scientist position. Let us know what excites you about the role!
Showcase Your Technical Skills: We’re looking for expertise in deep learning frameworks like PyTorch and TensorFlow. Make sure to mention any specific projects where you’ve used these tools, as well as your experience with Python and SQL. We love seeing practical examples!
Apply Through Our Website: Don’t forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it gives you a chance to explore more about StudySmarter and what we do!
How to prepare for a job interview at SIRE
✨Know Your Models Inside Out
Make sure you can discuss the machine learning models you've built in detail. Be ready to explain your approach to designing, developing, and deploying predictive models, especially those related to sports betting. Highlight any specific techniques or frameworks you've used, like CNNs or Transformers.
✨Showcase Your Leadership Skills
Since this is a leadership role, prepare examples of how you've managed and mentored teams in the past. Discuss your approach to guiding data scientists and engineers, and how you've fostered collaboration within your team. This will show that you're not just technically skilled but also a great leader.
✨Stay Current with Industry Trends
Demonstrate your knowledge of the latest trends in sports analytics and machine learning research. Be prepared to discuss recent advancements and how they could impact sports betting strategies. This shows that you're proactive and passionate about your field.
✨Communicate Clearly with Non-Technical Stakeholders
Practice explaining complex concepts in simple terms. You’ll need to translate your findings for stakeholders who may not have a technical background. Think of examples where you've successfully communicated intricate ideas and how that benefited your projects.
