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- Tasks: Lead the creation of a cricket modelling desk and tackle unique challenges in cricket analytics.
- Company: Join a dynamic team focused on innovative sports analytics.
- Benefits: Flexible work schedule with 4 days onsite and 1 day remote.
- Why this job: Combine your passion for cricket with cutting-edge predictive modelling techniques.
- Qualifications: 4+ years in predictive modelling, machine learning, and advanced programming skills.
- Other info: Collaborate with quants and developers in a fast-paced environment.
The predicted salary is between 36000 - 60000 £ per year.
Lead Cricket Modeller
4 days onsite, 1 day wfh
About The Role
My client is searching for a Lead Cricket Modeller to spearhead the creation and launch of their cricket modelling desk. This role requires a mix of thorough analyses and innovative modelling to address unique challenges in cricket analytics, where traditional methods might need adaptation or inventing. You will have the freedom to explore and develop your own ideas, collaborating with a team of quants, developers, and analysts to blend technical expertise with sports knowledge.
Requirements
They are looking for smart, ambitious individuals who enjoy solving challenging problems and can make pragmatic decisions in a dynamic environment. You should have:
- Over four years of practical experience in predictive modelling, machine learning, and probability theory, ideally within the sports, gaming, or betting sectors.
- Proficiency in a range of techniques, including Monte Carlo simulation, Bayesian modelling, mixed effects models, Kalman filters, Generalized Linear Models (GLMs), and time series forecasting. While you’re not expected to be an expert in every area, having a broad understanding of various methodologies and the trade-offs involved is vital.
- Advanced programming skills, with a preference for Python.
- Solid knowledge of SQL and relational databases.
- Demonstrated experience in working with new datasets, addressing data quality issues, and effectively managing imperfect data.
An excellent candidate will also:
- Understand and apply expected value and utility principles, both in evaluating betting scenarios and in prioritising projects or analyses
- Have a practical approach to problem-solving, balancing attention to detail with the ability to deliver MVPs quickly
- Be able to deliver projects independently, making informed and justifiable decisions, while also contributing effectively as part of a team
- Communicate complex models and analyses clearly to both technical and non-technical audiences
- Have an interest in cricket and sports analytics
Interested? Reach out to lucia.paolinelli@harringtonstarr.com
Lead Cricket Modeller employer: Harrington Starr
Contact Detail:
Harrington Starr Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Lead Cricket Modeller
✨Tip Number 1
Network like a pro! Get out there and connect with people in the cricket analytics space. Attend industry events, join relevant online forums, and don’t be shy to reach out on LinkedIn. You never know who might have the inside scoop on job openings!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your predictive modelling projects, especially those related to sports. This will not only demonstrate your expertise but also give potential employers a taste of what you can bring to their cricket modelling desk.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. Be ready to discuss your experience with Monte Carlo simulations or Bayesian modelling. Practice explaining complex concepts in simple terms – it’s all about making that connection!
✨Tip Number 4
Don’t forget to apply through our website! We’ve got loads of resources to help you land that Lead Cricket Modeller role. Plus, it shows you’re serious about joining our team and makes it easier for us to find your application.
We think you need these skills to ace Lead Cricket Modeller
Some tips for your application 🫡
Show Off Your Skills: Make sure to highlight your experience in predictive modelling and machine learning. We want to see how your skills can tackle the unique challenges in cricket analytics, so don’t hold back!
Be Creative: This role is all about innovation! Share any unique ideas or approaches you've used in past projects. We love seeing candidates who think outside the box and can adapt traditional methods.
Tailor Your Application: Don’t just send a generic CV. Tailor your application to reflect the specific requirements of the Lead Cricket Modeller role. Mention your programming skills in Python and your understanding of various modelling techniques.
Apply Through Our Website: We encourage you to apply through our website for a smoother process. It helps us keep track of applications and ensures you don’t miss out on any important updates!
How to prepare for a job interview at Harrington Starr
✨Know Your Cricket Analytics
Make sure you brush up on your cricket knowledge and analytics techniques. Familiarise yourself with predictive modelling, machine learning, and the specific methodologies mentioned in the job description. Being able to discuss how these apply to cricket will show your passion and expertise.
✨Showcase Your Problem-Solving Skills
Prepare examples of how you've tackled complex problems in previous roles. Think about times when you had to adapt traditional methods or invent new ones. This will demonstrate your practical approach and ability to deliver results in a dynamic environment.
✨Demonstrate Technical Proficiency
Be ready to discuss your programming skills, especially in Python and SQL. You might be asked to solve a technical problem or explain a model you've built. Practising coding challenges or discussing past projects can help you feel more confident during this part of the interview.
✨Communicate Clearly
Since you'll need to explain complex models to both technical and non-technical audiences, practice simplifying your explanations. Use analogies or straightforward language to convey your ideas. This will show that you can bridge the gap between data and decision-making effectively.
