Senior Data Scientist

The Rank Group
Greater London
8 months ago
Applications closed

Related Jobs

View all jobs

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

Company Description

We are – the Rank Group.

From the fun of Mecca Bingo to the experience of Grosvenor Casinos, from in-person to online, from local to global, players love the experiences our famous brands deliver.

This is such an exciting sector to be part of, bringing entertainment to millions in a culture of opportunity and growth.

Look at our careers site to find out more:

Job Description

We want to expand our Data Science function further within our well-established strong data-driven Centralised Analytical department. Our Data Science mission is to build machine models in the production environment relative to Marketing, Customer Insights, and Safer Gambling and establish a strong culture of data-driven decision-making in our organisation's strategy. 

We are looking for a well-established Data Scientist at all levels who wants new challenges. As a Senior Data Scientist, you will work using data engineering, statistical, and ML/AI approaches to uncover data patterns and build models. We use Microsoft tech stack, including Azure Databricks (Pyspark, python), and we are expanding our data science capabilities.

To be successful in the role, you will need to have extensive experience in data science projects and have built the professional skill to understand when an approach to a project is not working, to pause and change approach.

The Data Science department is currently a smaller team, with an ambition to grow, with a mix of a Data Scientists and ML engineers. Therefore, it is an excellent opportunity to grow, contribute and challenge yourself.

We are not an isolated function, so expect to work closely with business stakeholders, data engineers, marketing analysts and BI analysts to improve our existing models, create new models, and bring our expertise.

Core Responsibilities

Apply advanced statistical techniques and ML/AI models to development and production environments Collaborate with team members and stakeholders to build data science products that enable others to make business decisions

Qualifications

Postgraduate degree in a relevant discipline ( STEM, Maths, Statistics, Physics) or equivalent experience Good data modelling, software engineering knowledge, and strong knowledge of statistical, mathematical and ML modelling are a must at this stage. Skilful in writing well-engineered code Proven experience working with ML engineers and production systems (including Cloud platforms) Proven ability to analyse large sets and experience-built ML/AI models in production with the ability to translate them into insights and actionable business recommendations Great technical and commercial communication and collaboration skills with some presentation skills Passion for learning and keeping abreast of new technologies and data models

Additional Information

#LI-IZ1 #LI-Hybrid

Join us to unlock benefits and opportunities that will boost your career journey in a vibrant, inclusive and fulfilling work environment – so you can #BeYourself

Wellbeing@Rank is important... From hybrid working and colleague support networks to menopause support and weekly PepTalks, we’re here for you.

We’ll also invest in your growth by providing development opportunities, leadership training and cutting-edge industry certifications so you have the tools and resources to help you work, win and grow with us. 

Immerse yourself in new cultures and gain international exposure through our global business. Collaborate with colleagues from around the globe.

From pensions to bonus schemes, and private medical insurance to life insurance – we've got you covered. 

*Our benefits vary by brand and/or location. Please have a chat with your local Talent Acquisition specialist to find out what’s in place in your location.

The Rank Group are committed to being an inclusive employer, ensuring that we better understand and meet the needs and requirements of our candidates and customers. 

We aim to do this by facilitating fair and equal access to our services. If you require a reasonable adjustment to be made, please reach out to let us know ahead of your interview. 

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.