Data Scientist

bet365 Group
Stoke-on-Trent
2 days ago
Create job alert

As a Data Scientist, you will be responsible for developing machine learning solutions and performing statistical analysis to inform strategic, data-driven business decisions and initiatives.

Full-time

We are seeking a talented and motivated Data Scientist to join our Data Analytics team. The department is responsible for monitoring, analysing, and optimising key performance indicators across our range of Sports and Gaming products.

In this role, you will be instrumental in extracting valuable insights from vast datasets, developing predictive models, and contributing to data-driven decision-making across various business functions. You will work collaboratively with stakeholders from areas such as Fraud, Responsible Gambling, Trading and Branding to identify opportunities, solve complex problems, and build robust data solutions.

This is an exciting opportunity to apply cutting‑edge data science techniques in a fast‑paced, high‑volume, and globally recognised industry, utilising a modern and powerful tech stack.

This role is eligible for inclusion in the Company’s hybrid working from home policy.

Preferred Skills and Experience
  • Excellent analytical, problem‑solving, and critical thinking skills.
  • PhD degree in Computer Science, Statistics, Mathematics, or a related quantitative field.
  • Experience using core machine learning techniques, such as regressions, classification, clustering and deep learning.
  • Strong programming skills in languages such as Python, R, SQL.
  • Familiar with data science libraries and frameworks.
  • Detailed understanding of data mining, data warehousing, and data visualisation techniques.
  • Knowledge of Artificial Intelligence and it’s use within data science.
  • Strong communication skills with both technical and non‑technical audiences.
  • Knowledge of cloud computing, distributed systems, and big data technologies would be advantageous.
What you will be doing
  • Sourcing, cleaning, and validating diverse datasets from various internal and external sources.
  • Conducting in‑depth exploratory data analysis to uncover hidden patterns, identify trends, and generate actionable insights that inform strategic business decisions.
  • Developing and deploying robust statistical and machine learning models to address complex business challenges and drive innovative solutions.
  • Designing, implementing, and analysing A/B tests and other controlled experiments to measure the impact of new features, strategies, or models.
  • Contributing to the development and maintenance of scalable data science infrastructure.
  • Partnering closely with stakeholders to understand key business goals, and translate them into effective, data‑driven solutions.
  • Communicating complex findings and insights to technical and non‑technical audiences through visualisations, reports, and presentations.
  • Researching and championing innovative data science techniques, tools, and methodologies.
  • Fostering a culture of continuous learning and innovation within the wider Data Analytics team.
Bonus
  • Eye care and Flu Vaccinations
  • Life Assurance
Life at bet365

We are a unique global operator with passion and drive to be the best in the industry. Our values form the foundation of culture and shape the unique way that we work. People are our superpower and we support you to be the best you can be.


#J-18808-Ljbffr

Related Jobs

View all jobs

Data Scientist

Data Scientist

Data Scientist - New

Data Scientist / Software Engineer

Data Scientist - Contract - 12 months

Data Scientist (Globally Renowned Retail Group)

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.

Neurodiversity in Machine Learning Careers: Turning Different Thinking into a Superpower

Machine learning is about more than just models & metrics. It’s about spotting patterns others miss, asking better questions, challenging assumptions & building systems that work reliably in the real world. That makes it a natural home for many neurodivergent people. If you live with ADHD, autism or dyslexia, you may have been told your brain is “too distracted”, “too literal” or “too disorganised” for a technical career. In reality, many of the traits that can make school or traditional offices hard are exactly the traits that make for excellent ML engineers, applied scientists & MLOps specialists. This guide is written for neurodivergent ML job seekers in the UK. We’ll explore: What neurodiversity means in a machine learning context How ADHD, autism & dyslexia strengths map to ML roles Practical workplace adjustments you can ask for under UK law How to talk about neurodivergence in applications & interviews By the end, you’ll have a clearer sense of where you might thrive in ML – & how to turn “different thinking” into a genuine career advantage.

Machine Learning Hiring Trends 2026: What to Watch Out For (For Job Seekers & Recruiters)

As we move into 2026, the machine learning jobs market in the UK is going through another big shift. Foundation models and generative AI are everywhere, companies are under pressure to show real ROI from AI, and cloud costs are being scrutinised like never before. Some organisations are slowing hiring or merging teams. Others are doubling down on machine learning, MLOps and AI platform engineering to stay competitive. The end result? Fewer fluffy “AI” roles, more focused machine learning roles with clear ownership and expectations. Whether you are a machine learning job seeker planning your next move, or a recruiter trying to build ML teams, understanding the key machine learning hiring trends for 2026 will help you stay ahead.

Machine Learning Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK machine learning hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise shipped ML/LLM features, robust evaluation, observability, safety/governance, cost control and measurable business impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for ML engineers, applied scientists, LLM application engineers, ML platform/MLOps engineers and AI product managers. Who this is for: ML engineers, applied ML/LLM engineers, LLM/retrieval engineers, ML platform/MLOps/SRE, data scientists transitioning to production ML, AI product managers & tech‑lead candidates targeting roles in the UK.