At a Glance
- Tasks: Lead the design and deployment of machine learning models for sports betting predictions.
- Company: Join a cutting-edge sports analytics company with a remote-first culture.
- Benefits: Generous compensation package, flexible remote work, and opportunities for professional growth.
- Why this job: Make an impact in the exciting world of sports betting with innovative ML solutions.
- Qualifications: 5+ years in data science, leadership experience, and expertise in sports betting models.
- Other info: Collaborate with a dynamic team and stay ahead in the fast-paced sports analytics field.
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 employer: SIRE
Contact Detail:
SIRE Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Lead Data Scientist- Sports Betting ML
✨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. This could be a GitHub repo or a personal website. We want to see your hands-on experience and how you tackle real-world problems.
✨Tip Number 3
Prepare for interviews by brushing up on your technical skills and understanding of the latest ML trends. We recommend practising common interview questions and even doing mock interviews with friends. Confidence is key, so let’s nail that technical part!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are proactive about their job search. Let’s get you that Lead Data Scientist role!
We think you need these skills to ace Lead Data Scientist- Sports Betting ML
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the Lead Data Scientist role. Highlight your experience with sports betting models and machine learning frameworks like PyTorch and TensorFlow. We want to see how your skills align with our needs!
Showcase Your Projects: Include specific projects where you've designed, built, or deployed predictive models. We love seeing real-world applications of your work, especially in sports analytics. This helps us understand your hands-on experience!
Craft a Compelling Cover Letter: Your cover letter should tell us why you're passionate about sports betting and machine learning. Share your vision for how you can lead our team and improve our predictive edge. Let your personality shine through!
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows you’re keen on joining our team!
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 hands-on leadership role, be prepared to talk about your experience managing and mentoring teams. Share examples of how you've guided data scientists and engineers in past projects, and how you foster collaboration within your team.
✨Demonstrate Your Statistical Knowledge
Brush up on your statistics and probability theory. Be ready to discuss how these concepts apply to model validation and back testing. You might be asked to solve a problem on the spot, so having a solid grasp of these fundamentals will help you shine.
✨Stay Current with Industry Trends
Familiarise yourself with the latest advancements in sports analytics and machine learning research. Being able to discuss recent trends or breakthroughs in the field will show that you're passionate and engaged, which is crucial for a role that requires continuous improvement of predictive models.
