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- Tasks: Analyse football performance data and develop metrics for player evaluation.
- Company: Rapidly growing sports data consultancy with a passion for football.
- Benefits: Large bonus potential, pension, private medical, and free onsite gym access.
- Why this job: Combine your love for football and data science to make a real impact.
- Qualifications: Experience in data science and a passion for football.
- Other info: Enjoy hybrid working and a dynamic team environment.
The predicted salary is between 36000 - 60000 £ per year.
A rapidly growing sports data consultancy is seeking a Data Scientist specializing in football analytics. In this role, you will apply advanced data techniques to analyze performance data, develop metrics for player evaluation, and communicate findings to various stakeholders.
The position offers hybrid working, along with benefits such as a large bonus potential, pension, private medical, and access to a free onsite gym. If you are passionate about football and data science, this is an exciting opportunity to make an impact.
Football Analytics Data Scientist — Hybrid London employer: Jobster
Contact Detail:
Jobster Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Football Analytics Data Scientist — Hybrid London
✨Tip Number 1
Network like a pro! Reach out to people in the football analytics space on LinkedIn or at industry events. A friendly chat can open doors that a CV just can't.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your data analysis projects, especially those related to football. This will give you an edge and demonstrate your passion for the sport.
✨Tip Number 3
Prepare for interviews by brushing up on your knowledge of football metrics and analytics tools. Be ready to discuss how you can apply your skills to improve player evaluation and performance.
✨Tip Number 4
Don't forget to apply through our website! We love seeing candidates who are genuinely interested in joining our team. Plus, it makes tracking your application easier for us!
We think you need these skills to ace Football Analytics Data Scientist — Hybrid London
Some tips for your application 🫡
Show Your Passion for Football: Make sure to highlight your love for football in your application. We want to see how your passion drives your interest in data science and analytics, so don’t hold back!
Tailor Your CV and Cover Letter: Customise your CV and cover letter to reflect the skills and experiences that align with the job description. We’re looking for specific examples of how you’ve used data techniques in sports or similar fields.
Be Clear and Concise: When writing your application, keep it clear and to the point. We appreciate straightforward communication, so avoid jargon unless it’s relevant to the role. Make it easy for us to see your qualifications!
Apply Through Our Website: Don’t forget to submit your application through our website! It’s the best way for us to receive your details and ensures you’re considered for this exciting opportunity.
How to prepare for a job interview at Jobster
✨Know Your Football Stats
Make sure you brush up on the latest football analytics trends and metrics. Familiarise yourself with key performance indicators used in player evaluation, as this will show your passion for the sport and your expertise in data science.
✨Prepare Real-World Examples
Think of specific projects or analyses you've worked on that relate to football or sports data. Be ready to discuss how you applied advanced data techniques and what impact your findings had. This will demonstrate your practical experience and problem-solving skills.
✨Understand the Company’s Vision
Research the consultancy's recent projects and their approach to football analytics. Knowing their goals and how they use data can help you tailor your answers and show that you're genuinely interested in contributing to their mission.
✨Practice Communicating Findings
Since you'll need to communicate complex data insights to various stakeholders, practice explaining your analyses in simple terms. Use clear examples and avoid jargon to ensure your message is understood, showcasing your ability to bridge the gap between data and decision-making.
