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- Tasks: Join a team to enhance predictive models and tackle sports analytics challenges.
- Company: Work with a collaborative team of data scientists and sports analysts.
- Benefits: Enjoy generous leave, health benefits, gym memberships, and learning budgets.
- Why this job: Dive into innovative modeling while exploring your ideas in a dynamic environment.
- Qualifications: 3+ years in predictive modeling; strong Python skills required.
- Other info: Flexible work schedule: 4 days onsite, 1 day remote.
The predicted salary is between 42000 - 84000 £ per year.
Football Quant
4 days onsite, 1 day wfh
About the Role
My client is seeking a Football Quantitative Analyst to join their prediction team. You will use extensive datasets to enhance predictive models, explore new methodologies, and develop solutions. This role involves a blend of in-depth analysis and innovative modelling to tackle unique challenges in sports analytics. You will be joining a collaborative team of data scientists and sports analysts, as well as having the freedom to explore and develop individual ideas.
Requirements
- 3+ years of experience in predictive modelling, machine learning, and probability theory, preferably in the sports or gaming/betting industries.
- Familiarity with techniques such as Monte Carlo simulation, Bayesian modelling, mixed effects models, Kalman filters, GLMs, and time series forecasting.
- Strong programming skills, particularly in Python.
- Experience in exploring new datasets, identifying data quality issues, and handling imperfect data effectively.
Preferred Qualifications
- Understanding of expected value and utility principles.
- Practical problem-solving approach, balancing detail with quick delivery of MVPs.
- Ability to independently deliver projects and make informed decisions..
What you\’ll get
- Generous annual leave (including bank holidays)
- Competitive pension contributions
- Health and well-being benefits
- Subsidised gym membership and meals
- Learning and development budgets
- Regular sports activities with colleagues
Interested? Apply to this role or send your CV directly to luica.paolinelli@harringtonstarr.com to be considered.
Football Quant employer: Harrington Starr
Contact Detail:
Harrington Starr Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Football Quant
✨Tip Number 1
Make sure to showcase your experience with predictive modeling and machine learning in your conversations. Highlight specific projects where you've successfully applied these techniques, especially in sports analytics or related fields.
✨Tip Number 2
Familiarize yourself with the latest methodologies in sports analytics, such as Monte Carlo simulations and Bayesian modeling. Being able to discuss these techniques confidently will demonstrate your expertise and passion for the field.
✨Tip Number 3
Prepare to discuss how you've handled imperfect data in past projects. Sharing specific examples of how you identified and resolved data quality issues can set you apart from other candidates.
✨Tip Number 4
Show your programming skills, particularly in Python, by being ready to talk about relevant coding projects. If possible, bring examples of your work that demonstrate your ability to deliver quick MVPs while maintaining attention to detail.
We think you need these skills to ace Football Quant
Some tips for your application 🫡
Understand the Role: Make sure to thoroughly read the job description for the Football Quant position. Understand the key responsibilities and required skills, especially in predictive modelling and programming in Python.
Highlight Relevant Experience: In your CV and cover letter, emphasize your 3+ years of experience in predictive modelling and any relevant work in sports or gaming/betting industries. Be specific about the methodologies you have used, such as Monte Carlo simulation or Bayesian modelling.
Showcase Your Skills: Demonstrate your strong programming skills, particularly in Python. Include examples of projects where you explored new datasets or handled data quality issues effectively.
Personalize Your Application: Tailor your application to reflect your understanding of the company’s needs and how your practical problem-solving approach can contribute to their prediction team. Mention your ability to independently deliver projects and make informed decisions.
How to prepare for a job interview at Harrington Starr
✨Showcase Your Technical Skills
Be prepared to discuss your experience with predictive modeling and machine learning techniques. Highlight specific projects where you've used methods like Monte Carlo simulation or Bayesian modeling, and be ready to explain your approach and the outcomes.
✨Demonstrate Problem-Solving Abilities
Prepare examples that illustrate your practical problem-solving skills. Discuss how you've tackled challenges in sports analytics, focusing on your ability to balance detail with quick delivery of minimum viable products (MVPs).
✨Familiarize Yourself with the Team's Work
Research the company’s prediction team and their recent projects. Understanding their methodologies and challenges will allow you to ask insightful questions and show your genuine interest in contributing to their goals.
✨Discuss Data Quality Management
Be ready to talk about your experience with exploring new datasets and handling data quality issues. Share specific strategies you've employed to manage imperfect data effectively, as this is crucial for a role focused on enhancing predictive models.
