Quantitative Data Analyst

Betsson Group
London
1 month ago
Applications closed

Related Jobs

View all jobs

Data Analyst (Cars Data Science & Analytics) - Manchester, UK

Academy Data Analyst - Halewood

Data Analyst - Farming Operations

Data Analyst Senior Consultant, Assistant Manager, Manager - Belfast

Data Analyst - Farming Operations

Senior Data Governance Analyst

Join our team and shape the future of sports betting! We're seeking a highly motivated and innovative Quantitative Analyst to develop and maintain cutting-edge statistical models. You'll play a crucial role in enhancing our market offerings and driving our success in the dynamic world of sports betting. This is an opportunity to leverage your expertise in data science, machine learning, and AI to build models that predict outcomes, manage risk, and unlock new possibilities in micro-markets and derivatives.


A taster of what you will be involved with:

As a Quantitative Analyst, you will be a key contributor to our quantitative modelling strategy across multiple areas, including:



  • Core Sports Betting Models: Develop and refine models for predicting match outcomes, incorporating advanced statistical methods, machine learning algorithms, and AI techniques.
  • Player Statistics Models: Build and maintain sophisticated models that analyze player performance and predict individual contributions, opening up exciting new betting markets.
  • Risk Management Models: Design and implement robust risk models to optimize our exposure and ensure the long-term sustainability of our operations.
  • Market Expansion: Contribute to the development of innovative market offerings, including derivatives and micro-markets, by creating the necessary underlying models.
  • Technology Modernization: Collaborate on the modernization of our modeling technologies, exploring and implementing new tools and platforms to enhance efficiency and scalability.
  • Data-Driven Innovation: Contribute to the development of new data-driven methodologies, pushing the boundaries of what's possible in sports betting analytics.
  • Data Collaboration: Work closely with the data engineering team to optimize data storage and retrieval processes, ensuring seamless access to the information needed for model development.
  • Data Acquisition: Identify and collect relevant data sources to fuel model development and improvement.
  • Knowledge Sharing: Document model methodologies clearly and concisely, effectively communicating complex technical concepts to both technical and non-technical stakeholders across the business.
  • Performance Analysis: Collaborate with the performance analysis team to identify areas where models can be improved and contribute to the ongoing refinement of our analytical tools.

What we are looking for

You are a highly analytical and results-oriented individual with a passion for sports and a deep understanding of quantitative modelling. You thrive in a fast-paced environment and are eager to contribute to a team that is pushing the boundaries of sports betting analytics.



  • Education: PhD, Masters, or Bachelor’s degree in a STEM field (e.g., Mathematics, Statistics, Computer Science, Physics, Engineering).
  • Programming Skills: Proficiency in C# and Python is essential.
  • Modelling Expertise: Strong knowledge of statistical and machine learning modelling techniques.
  • Communication Skills: Excellent communication skills, with the ability to clearly explain complex technical information to diverse audiences.
  • Industry Knowledge: A solid understanding of the sports betting industry, sports trading, and sports modelling.
  • Problem-Solving: Demonstrated ability to solve complex problems and provide clear justifications for chosen methodologies.
  • Modelling Experience: Proven experience in building and deploying mathematical models.
  • Data Skills: Proficient in data retrieval and statistical analysis.
  • Experience: 5+ years of experience in a quantitative role.

Bonus Points:



  • Experience working in the sports betting industry.
  • Mentoring and team leadership skills.
  • Experience developing quantitative models using machine learning and AI.
  • Strong knowledge and skills in data engineering (SQL).

What we offer

  • A chance to be part of a dynamic and innovative team at the forefront of the sports betting industry.
  • The opportunity to work on challenging and impactful projects.
  • A competitive salary and benefits package.
  • A collaborative and supportive work environment.

If you're ready to take your quantitative skills to the next level and revolutionize the world of sports betting, we encourage you to apply!


Challenge accepted?

By submitting your application, you understand that your personal data will be processed as set out in our Privacy Policy


#J-18808-Ljbffr

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.