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- Tasks: Lead the creation of a cricket modelling desk, blending analysis with innovative solutions.
- Company: Join a dynamic team focused on sports analytics and predictive modelling.
- Benefits: Enjoy a flexible work schedule with 4 days onsite and 1 day working from home.
- Why this job: Explore your ideas in a collaborative environment while tackling unique challenges in cricket analytics.
- Qualifications: 4+ years in predictive modelling; strong skills in Python, SQL, and various modelling techniques.
- Other info: Ideal for those passionate about cricket and eager to make an impact in sports analytics.
The predicted salary is between 48000 - 72000 £ 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
Familiarize yourself with the latest trends in cricket analytics and predictive modelling. This will not only help you understand the current landscape but also allow you to bring fresh ideas to the table during discussions with the team.
✨Tip Number 2
Network with professionals in the sports analytics field, especially those who have experience in cricket. Engaging with others can provide insights into the industry and may even lead to referrals or recommendations.
✨Tip Number 3
Prepare to discuss your previous projects involving predictive modelling and machine learning. Be ready to explain your thought process, the challenges you faced, and how you overcame them, as this will demonstrate your problem-solving skills.
✨Tip Number 4
Showcase your passion for cricket and sports analytics during your interactions. Whether it's through personal projects, blogs, or discussions, demonstrating your enthusiasm can set you apart from other candidates.
We think you need these skills to ace Lead Cricket Modeller
Some tips for your application 🫡
Understand the Role: Before applying, make sure you fully understand the responsibilities and requirements of the Lead Cricket Modeller position. Tailor your application to highlight your relevant experience in predictive modelling and sports analytics.
Highlight Relevant Experience: In your CV and cover letter, emphasize your practical experience with predictive modelling, machine learning, and any specific techniques mentioned in the job description, such as Monte Carlo simulation or Bayesian modelling.
Showcase Programming Skills: Make sure to detail your programming skills, particularly in Python, and your experience with SQL and relational databases. Provide examples of projects where you've applied these skills effectively.
Communicate Clearly: When writing your application, focus on how you can communicate complex analyses to both technical and non-technical audiences. This is crucial for the role, so provide examples of how you've done this in the past.
How to prepare for a job interview at Harrington Starr
✨Showcase Your Analytical Skills
Be prepared to discuss your experience with predictive modelling and machine learning. Highlight specific projects where you applied techniques like Monte Carlo simulation or Bayesian modelling, and be ready to explain the outcomes and decisions made.
✨Demonstrate Technical Proficiency
Since advanced programming skills in Python are crucial, consider preparing a small coding exercise or discussing a relevant project. Familiarize yourself with SQL and how you've used it to manage data quality issues in past roles.
✨Communicate Clearly
Practice explaining complex models and analyses in simple terms. You may encounter both technical and non-technical interviewers, so being able to adapt your communication style is key to demonstrating your collaborative spirit.
✨Express Your Passion for Cricket
Since the role involves cricket analytics, share your interest in the sport. Discuss any relevant experiences or insights you have about cricket that could inform your modelling work, showing that you can blend sports knowledge with technical expertise.
