At a Glance
- Tasks: Dive into data to uncover insights and influence strategic decisions in sports betting.
- Company: Join Spotlight Sports Group, a leading media and tech company in the sports industry.
- Benefits: Enjoy competitive pay, private medical insurance, generous holiday, and well-being initiatives.
- Why this job: Make an impact with your analytical skills while working in a dynamic, hybrid environment.
- Qualifications: Strong SQL and Python skills, plus a knack for storytelling with data.
- Other info: Opportunities for career growth and volunteering days to give back.
The predicted salary is between 28800 - 43200 £ per year.
Spotlight Sports Group is a global media and technology company specialising in content and data within sports betting, horse racing and fantasy sports. With over 400 employees, the group operates multiple award-winning brands, including Racing Post, Pickswise, myracing and Free Super Tips. We partner with leading operators across the betting industry to produce and build multilingual, best-in-class digital products and content to engage and educate customers.
This Data Analyst role sits within the Data Intelligence Unit (DIU) and is focused on insight generation, analytical problem-solving, and decision support, not just reporting. The role is responsible for improving the organisation's understanding of customers, behaviours, and performance drivers by moving beyond surface-level trends to explain underlying causes, quantify impact, and support evidence-based decision-making across Content, Commercial, CRM, Product, and Leadership teams.
This role is particularly well suited to an analyst who enjoys working with complex behavioural data, thrives in ambiguous problem spaces, and wants to influence strategy through rigorous analysis. An interest in sport and betting markets is helpful for context, but analytical curiosity and critical thinking are essential. This role is a hybrid role based in our Central London Office near Southbank 3 days a week, with 2 days from home.
Key accountabilities:- Insight Generation: Go beyond BAU reporting to proactively identify trends, patterns, and opportunities in customer behaviour, lifecycle progression, segmentation, and product engagement, linking insight to commercial and strategic outcomes.
- Stakeholder Collaboration: Work closely with squads, product teams, and leadership to understand challenges, shape hypotheses, and deliver insights that influence strategic decisions.
- Data Storytelling: Translate complex datasets and analysis into clear, compelling narratives that drive action, ensuring insights are accessible and meaningful for stakeholders with varying levels of data fluency.
- Impact Measurement: Develop and implement robust measurement frameworks, including test-and-learn approaches, to evaluate the success of business changes and ensure decisions are evidence-based and continuously refined.
- Championing a Data-Driven Culture: Advocate for data as a strategic asset, challenging assumptions and embedding insight-led decision-making across Content, Product, CRM, and Commercial teams.
- Automation for Efficiency: Streamline and automate recurring reporting and analysis, maximising time spent on high-value insight generation rather than manual data preparation.
Required Skills:
- Strong SQL skills, with the ability to query, join, and manipulate large and complex datasets.
- Strong working knowledge of Python for data analysis and automation (e.g. pandas, numpy).
- Ability to understand new datasets quickly and extract meaningful insights.
- Experience working with large, complex datasets across multiple sources.
- Proficiency in data visualisation tools (Looker Studio), with a focus on creating actionable insights rather than static reporting.
- Hands-on experience with product analytics tools (e.g., Google Analytics, Amplitude, Mixpanel).
- Ability to work independently, defining topics for analysis and driving them to completion.
- Strong communication skills, with the confidence to present findings and challenge assumptions.
Desirable:
- Background in Mathematics, Statistics, Data Science, or a related quantitative discipline.
- An interest in sport, betting markets, or data-driven digital products.
We offer a range of well-being initiatives, including private medical insurance, excellent parental leave, a working globally policy, mental health support, assistance programs, and social gatherings. We also provide a pension scheme and various other benefit schemes. Plus, we all get our birthdays off work and enjoy 25 days of holiday per year.
We've also got you covered with life assurance and exclusive perks like the Star card and our Step Further Awards (our employee recognition program) to recognise your dedication. For those working via the hybrid model (in the office and at home) we've made commuting easier with our Season Ticket Loan and Cycle to Work Scheme.
You can also take advantage of complimentary access to our Racing Post Members Club, complete with an Ultimate Membership. We believe in making a positive impact beyond the workplace, and you'll have the chance to volunteer two days per year with our charity partner.
Data Analyst in London employer: Spotlight Sports Group
Contact Detail:
Spotlight Sports Group Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Analyst in London
✨Tip Number 1
Network like a pro! Reach out to people in the industry, especially those at Spotlight Sports Group. A friendly chat can open doors and give you insights that a job description just can't.
✨Tip Number 2
Show off your analytical skills in interviews! Prepare examples of how you've tackled complex data problems before. We want to see your thought process and how you can turn data into actionable insights.
✨Tip Number 3
Don’t just apply; engage! When you submit your application through our website, follow up with a quick email expressing your enthusiasm for the role. It shows initiative and keeps you on our radar.
✨Tip Number 4
Be ready to discuss sports and betting trends! Having a genuine interest in these areas can set you apart. It’s not just about numbers; it’s about understanding the context behind them.
We think you need these skills to ace Data Analyst in London
Some tips for your application 🫡
Show Your Analytical Skills: When you're writing your application, make sure to highlight your analytical skills. We want to see how you can go beyond just reporting and really dig into the data to uncover insights that can drive decisions.
Tailor Your Application: Don’t just send a generic application! Tailor it to the Data Analyst role by mentioning your experience with SQL, Python, and data visualisation tools. We love seeing how your background aligns with what we do at Spotlight Sports Group.
Tell a Story with Data: Remember, data storytelling is key! Use your application to demonstrate how you've turned complex datasets into clear narratives in the past. This will show us that you can communicate insights effectively to different stakeholders.
Apply Through Our Website: Finally, don’t forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. We can’t wait to see what you bring to the table!
How to prepare for a job interview at Spotlight Sports Group
✨Know Your Data Tools
Make sure you're well-versed in SQL and Python, as these are crucial for the Data Analyst role. Brush up on your skills with querying large datasets and using libraries like pandas and numpy to manipulate data effectively.
✨Understand the Business Context
Familiarise yourself with the sports betting and fantasy sports landscape. Knowing how data impacts customer behaviour and business decisions will help you provide relevant insights during the interview.
✨Practice Data Storytelling
Prepare to explain complex data analyses in simple terms. Think of examples where you've turned data into actionable insights, and be ready to discuss how you can communicate these findings to stakeholders with varying levels of data fluency.
✨Show Your Analytical Curiosity
Demonstrate your passion for digging deeper into data. Be prepared to discuss how you've approached ambiguous problems in the past and the methods you used to uncover underlying trends and insights.