Data Scientist

Singular Recruitment
Slough
7 months ago
Applications closed

Related Jobs

View all jobs

Data Scientist

Data Scientist

Data Scientist

Data Scientist

Data Scientist (Government)

Data Scientist (NLP & LLM Specialist)

Data Scientist(one day a week in the central London office)


Sports Analytics


This role is a unique opportunity for aData Scientistto combine technical challenges with creativity in a collaborative, high-standard work environment.


By joining this team, you’ll not only be part of a creative and open work culture focused on innovation and excellence but also have the chance to work with and collaborate with some of the most well-known footballers in the industry.


This position offers significant opportunities for professional growth within sports analytics and the potential to impact sports performance through advanced technology, making it an ideal setting for those passionate about leveraging cutting-edge technology to make meaningful contributions in the world of sports analytics.


Key responsibilities for the role of Data Scientist include:


  • Collect, clean, and process football-related data from various sources.
  • Develop and implement statistical models and algorithms to analyze player performance and match outcomes.
  • Create detailed reports and visualizations to communicate insights and recommendations to technical and non-technical stakeholders.
  • Collaborate with football analysts, coaches, and other stakeholders to understand their needs and provide actionable insights.
  • Stay updated with the latest trends and advancements in sports analytics and data science.


As the selected Data Scientist, your background will include:


  • 3+ yearsindustry experience in aData Sciencerole and a strong academic background
  • Python Data Science Stack:Advanced proficiency inPython, includingpandas,NumPy,scikit-learn, andJupyter Notebooks.
  • Statistical & ML Modelling:Strong foundation in statistical analysis and proven experience applying a range of machine learning techniques to solve business problems (e.g., regression, classification, clustering, time-series forecasting). Practical experience withKerasorPyTorchis required.
  • Full-Stack Deployment:Demonstrable experience taking models to production, including building and deploying APIs withFastAPIand usingVertex AIfor ML workflows.
  • Visualization & Communication:Ability to create clear visualizations and effectively communicate technical findings to non-technical stakeholders.


Highly desirable skills include:


  • Football Analytics Domain:Significant plus if experienced with football datasets (event, tracking, etc.) and visualization libraries likemplsoccer.
  • Advanced MLOps & Modelling:Deeper experience with theVertex AIlifecycle (especiallyPipelines) and advanced modelling techniques relevant to football (player valuation, tactical analysis).
  • Bayesian Modelling:Experience with probabilistic programming (e.g., PyMC).
  • Stakeholder Management:Proven success working directly with business stakeholders to define and deliver impactful solutions.


What They Offer


  • Work that impacts elite football performance and club-wide success
  • Access to real-world sports data and performance analytics
  • Flexible working options (hybrid/remote depending on role)
  • Opportunity to grow with a digital-first team inside a world-renowned club

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.