Sports Modelling Data Scientist (relocation to Costa Rica)

Exacta Solutions Ltd

Glasgow

10 months ago

Applications closed

Related Jobs

View all jobs

Quantitative Analyst

HR Data Analyst

About the Job

Our client is seeking aData Scientist with a strong focus on Sports Modellingto join their Research & Development team. You will play a key role in enhancing and developing state-of-the-art sports models. This position sits at the intersection ofdata science, engineering, software development, and mathematics, offering the opportunity to work on highly impactful machine learning solutions within the iGaming industry.

Key Responsibilities

Build and maintain robust, high-quality datasets for machine learning models.
Design and implement automated algorithms and advanced machine learning tools.
Collaborate with a cross-functional team to solve complex problems and drive innovation.

Skills & Qualifications

Solid experience withmachine learning, particularly withneural networks.
Strong programming skills, preferably inPython; knowledge ofMatlabis a plus.
Expertise indata shaping, preprocessing, and feature engineering.
Excellent command ofEnglish, both written and spoken.
Prior experience in the iGaming or sports modelling industry is a strong advantage.

What’s on Offer

A chance to work with an innovative, forward-thinking company leading in its field.
A dynamic and collaborative team environment.
Competitive compensation package.
Clear opportunities for professional development and career growth.
Access to aCompany Doctor.
Relocation packageavailable for international candidates.

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

Industry Insights

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

Apr 9, 2026

Products

Where to Advertise Data Science Jobs in the UK (2026 Guide)

Advertising data science jobs in the UK requires a different approach to most technical hiring. Data science spans a broad and often misunderstood spectrum — from statistical modelling and experimental design through to machine learning engineering, product analytics and AI research. The strongest candidates identify firmly with specific subdisciplines and are frustrated by adverts that conflate data scientist with data analyst, business intelligence developer or machine learning engineer. General job boards produce high application volumes for data roles but consistently fail to match specialist data science profiles with the right opportunities. This guide, published by DataScienceJobs.co.uk, covers where to advertise data science roles in the UK in 2026, how the main platforms compare, what employers should expect to pay, and what the data says about hiring across different role types.

Mar 24, 2026

Jobs

New Data Science Employers to Watch in 2026: UK and International Companies Leading Analytics and AI Innovation

Data science has emerged as one of the most transformative forces across industries, turning raw information into actionable insights, predictive models, and AI-powered solutions. In 2026, the UK is witnessing a surge in organisations where data science is not just a support function but the core of their products and services. For professionals exploring opportunities on www.DataScience-Jobs.co.uk , identifying these employers early can provide a competitive advantage in a market with high demand for advanced analytics and machine learning expertise. This article highlights new and high-growth data science employers to watch in 2026, focusing on UK startups, scale-ups, and global firms expanding their data science operations locally. All of the companies included have recently raised investment, won high-profile contracts, or significantly scaled their analytics teams.

Feb 10, 2026

Careers

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.