At a Glance
- Tasks: Build and enhance models for player evaluation using cutting-edge data techniques.
- Company: Join Football Radar, a leader in football analytics with a dynamic start-up vibe.
- Benefits: Enjoy 33 days holiday, health perks, and learning budgets for personal growth.
- Other info: Collaborative environment with opportunities for career advancement and engagement activities.
- Why this job: Make a real impact in football analytics while exploring innovative modelling techniques.
- Qualifications: STEM degree, strong predictive modelling skills, and a passion for football.
The predicted salary is between 40000 - 60000 £ per year.
As a Data Scientist on our Club Services team, you'll work with high-frequency tracking and event data to build and improve the models behind our player evaluation framework. The problems are often difficult with no clear answer, so you'll need to make defensible modelling decisions, quantify your uncertainty, and then make your work robust enough to run in production and be trusted in real recruitment decisions.
To achieve this, you will have the freedom to explore and develop your own ideas while working collaboratively with a team of data scientists, developers, and analysts, to combine technical expertise with football knowledge.
Responsibilities
- Build and enhance models for player evaluation using high-frequency tracking and event data.
- Make defensible modelling decisions and quantify uncertainty to support production-level and recruitment decisions.
- Collaborate with data scientists, developers, and analysts to combine technical expertise with football knowledge.
- Explore and develop new ideas, including techniques such as Bayesian modelling, mixed effects models, GLMs, and neural networks.
- Take projects from research into production and deploy them to the cloud.
- Communicate complex models and analyses clearly to both technical and non-technical audiences.
- Work collaboratively across teams, sharing ideas early, and incorporating feedback from both technical and football-focused colleagues.
Qualifications
- A Bachelor's, Master's, or PhD in a STEM subject.
- Solid understanding of predictive modelling, machine learning, and probability theory.
- Familiarity with techniques such as Bayesian modelling, mixed effects models, GLMs, and neural networks.
- Ability to communicate complex models and analyses clearly to both technical and non-technical audiences.
- Comfort working collaboratively across teams, sharing ideas early, and taking onboard feedback.
- Proficiency in Python for data analysis and modelling.
- Experience working with SQL and relational databases.
- Interest in football and sports analytics.
Senior Candidate Expectations
- 3+ years of experience applying predictive modelling and machine learning in industry, with exposure to football through professional work or substantial personal projects.
- Experience working with high volume tracking-data datasets, with a comprehensive understanding of the challenges and possible approaches associated with them.
- A practical approach to problem-solving, balancing attention to detail with the ability to deliver MVPs quickly.
- Ability to deliver projects independently, making informed and justifiable decisions, while also contributing effectively as part of a team.
- Experience taking models from research into production, and deploying them to the cloud.
Benefits
- Half-yearly bonus opportunities based on company performance.
- 33 days holiday (including bank holidays).
- Competitive contribution matched pensions.
- Health and well-being benefits: Private Medical Insurance (including excess coverage), Health Cash Plan.
- Subsidised gym membership.
- Daily subsidised office meals.
- Learning and development budgets to invest in your personal growth.
- Company and team-led engagement activities throughout the year.
Company Mission
At Football Radar, our mission is to be the world‑leading provider of football analytics. For over a decade, we have combined predictive modelling techniques with expert analysis and our proprietary datasets to deliver insights that drive success for our betting clients and football clubs. By combining the agility of a start‑up with the stability of an established business, we've created an environment where innovation and long‑term success go hand in hand.
Club Data Scientist (Junior or Senior) in City of Westminster employer: Football Radar
At Football Radar, we pride ourselves on being an exceptional employer that fosters a collaborative and innovative work culture. Our Club Data Scientist role offers the unique opportunity to engage with cutting-edge football analytics while enjoying generous benefits such as 33 days of holiday, private medical insurance, and learning budgets for personal growth. With a focus on employee well-being and a commitment to professional development, we empower our team members to explore their ideas and contribute meaningfully to our mission of leading the football analytics industry.
StudySmarter Expert Advice🤫
We think this is how you could land Club Data Scientist (Junior or Senior) in City of Westminster
✨Get Involved in Data Science Meetups
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Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Football Radar.
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We think you need these skills to ace Club Data Scientist (Junior or Senior) in City of Westminster
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Football Radar, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Football Radar. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Football Radar
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Football Radar!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
