At a Glance
- Tasks: Transform complex data into actionable insights and drive business growth.
- Company: Join a forward-thinking analytics team in a dynamic tech environment.
- Benefits: Competitive salary, flexible working hours, and opportunities for professional growth.
- Other info: Collaborative culture with a focus on innovation and career advancement.
- Why this job: Make a real impact by leveraging data to shape strategic decisions.
- Qualifications: 3+ years in data science, strong SQL and Python skills required.
The predicted salary is between 49500 - 60500 £ per year.
We are seeking an inquisitive and impact-driven Data Scientist to join our Analytics team. In this role, you won't just build models—you will translate complex, unstructured data into actionable strategic directions that drive our core business forward. You will partner closely with Product, Engineering, Marketing, and Operations to build predictive frameworks and discover growth opportunities.
Key Responsibilities
- Experimentation & A/B Testing: Design, execute, and analyze rigorous A/B and multivariate tests to measure feature performance, user retention, and business impact.
- Predictive Analytics & Machine Learning: Build, validate, and maintain scalable predictive models (e.g., churn, forecasting, customer lifetime value (CLV) and user segmentation) to guide business decisions.
- Exploratory Data Analysis: Deep dive into large, complex datasets to identify trends, user behavioural patterns, operational bottlenecks, and unexpected growth opportunities.
- Features Definition & Measurement: Establish and standardize key features to track for modelling and customer data platform.
- Cross-Functional Strategy: Partner with product managers, executive leadership, and business stakeholders to turn open-ended questions into structured quantitative analyses.
- Analytics AI Agents: Design, deploy, and manage autonomous AI agents that automate repetitive analytics tasks and generate insights.
- Data Pipelines & Quality Assurance: Partner with Data Engineering to curate analytical datasets, build clean data transformations (dataform/SQL), and uphold data governance standard practices.
Qualifications & Skills
- Technical Competencies
- Advanced SQL: Highly proficient in writing optimized, complex queries across large cloud data warehouses (e.g., Snowflake, BigQuery).
- Programming & Modelling: Strong proficiency in Python for statistical modeling, forecasting, and machine learning.
- Statistics & Math: Deep understanding of statistical inference, regression analysis, probability theory, decision trees, and hypothesis testing (p-values, confidence intervals, Bayesian testing etc.).
- Data Visualization: Hands-on experience building interactive dashboards using tools like Power BI, Looker, or Python libraries.
- Experience & Soft Skills
- Experience: 3+ years of experience in a quantitative analytics or data science role in a fast-paced environment.
- Business Acumen: Demonstrated track record of framing business problems into mathematical/statistical frameworks and outputting actionable recommendations.
- Communication: Ability to distill complex statistical concepts into clear, plain language for non-technical executives.
Nice-to-Haves
- Experience with data building tools (i.e. Dataform) for data transformation workflows.
- Experience with digital product analytics tools (i.e., Google Analytics).
- Experience with AI Agents i.e., Vertex AI (Gemini Enterprise Agent Platform).
Data Scientist employer: Oddschecker
At FairPlay Sports Media, we pride ourselves on being an exceptional employer that champions diversity and fosters an inclusive, fast-paced work culture. Located in the vibrant heart of London, our team enjoys unique benefits such as a generous unlimited holiday plan, a £1,000 annual training fund for professional development, and free healthcare coverage. Join us to be part of a dynamic environment where your passion for sports can thrive, and where you can grow alongside industry leaders in the exciting world of sports betting.
StudySmarter Expert Advice🤫
We think this is how you could land Data Scientist
✨Get Involved in Data Science Meetups
Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like Oddschecker!
✨Show Off Your Projects
Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like Data Scientist at Oddschecker.
✨Leverage Professional Networks
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 Oddschecker.
✨Apply Directly through Our Website
When you find a suitable opening like Data Scientist at Oddschecker, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!
We think you need these skills to ace Data Scientist
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 Oddschecker, 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 Oddschecker. 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 Oddschecker
✨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 Oddschecker!
✨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.
