Sports Data Scientist

Hadte Group
London
2 months ago
Applications closed

Related Jobs

View all jobs

Junior Data Scientist: Sports Analytics & Trading

Senior Data Scientist - Real-Time Sports Betting & ML

Elite Sports Data Scientist — ML for Performance

Senior Data Scientist, Sports

Senior Data Scientist, Sports

Senior Data Scientist, Sports

Data Scientist (Multiple roles, various seniority) | Sports Data (Pick YOUR sport)

We’re helping several sports data leaders, specialised in turning complex sporting datasets into actionable insights that drive smarter, evidence-based decision-making. Each team combines technical expertise with a genuine passion for the sport, ensuring that the insights they deliver are both rigorous and practical!


Cricket, Tennis, Horse Racing or Football (Soccer) focussed...


Essential Skills (Across the ground/ court/ track/ pitch):

  • Python programming.
  • Practical experience working with sport data (either professionally or within personal projects).
  • Exposure with statistical methods, sports modelling and machine learning, with the ability to apply them to real-world sporting problems.
  • Genuine passion for Cricket, Tennis, Horse Racing or Football and a curiosity-driven approach to understanding the sport.


Day-to-Day Duties (Throughout the venue):

  • Analyse sport datasets using advanced data science techniques to uncover insights on team and player performance, tactics and trends.
  • Clean, process and analyse the sport datasets to create sport performance metrics.
  • Build predictive sport models and analytical frameworks.
  • Contribute to internal research projects exploring new metrics, analytical methods or innovative applications of sport data.


Benefits (Beyond the match/ race/ game):

  • An impact from day one.
  • Direct involvement with sport and the chance to apply data science to the real-world sporting calendar.
  • Creative freedom to experiment and innovate.
  • Supportive, collaborative culture that encourages learning and growth.
  • A vibrant team culture with a shared passion for sports.
  • Access to the latest tools and technologies.
  • Collaborative, flexible, hybrid working model.
  • Competitive salary, plus an endless list of benefits.

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 Machine Learning Tools Do You Need to Know to Get a Machine Learning Job?

Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more. With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive. Here’s the honest truth most machine learning hiring managers won’t say out loud: 👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know. Tools are important — no doubt — but context, judgement and outcomes matter far more. So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped. This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.