Data Analyst

Salt
London
1 year ago
Applications closed

Related Jobs

View all jobs

Data Analyst (Cars Data Science & Analytics) - Manchester, UK

Junior Data Scientist / Data Analyst

Data Scientist, Machine Learning Engineer, Data Analyst, Data Engineer, AI Engineer, Business Intelligence Analyst, Data Architect, Analytics Engineer, Research Data Scientist, Statistician, Quantitative Analyst, ML Ops Engineer, Applied Scientist, Insigh

Data Scientist

Data Scientist

Data Scientist - Investigations

Customer Data Analyst

Retail

£60,000 - £70,000

London | Hampshire - 2 days a month


The Company:

A SUPER established UK retailer is looking for a Customer Data Manager to join their digital and data transformation efforts. With substantial investment in their data and analytics team, this position will play a crucial role in building a foundation for accurate and reliable datasets.


This role is a newly created role so this is a roll your sleeves up opportunity where you can really see the impacts of your actions.


Ideally, you’ll be well versed with large amounts of customer data.



The Role:

  • Under the guidance of the team’s data manager, create new models and enhance existing ones to analyse customer behavior. This may include churn models, time series forecasting, customer base value assessments, and other specialized, targeted models.
  • Contribute to develop new and improve existing customer features that can used in marketing activity and across the business, to both help target and describe customers.
  • Deliver complex descriptive and predictive customer behavioural analytics.


Requirements:

  • You have working knowledge in eitherPython or Rwith a strong ability to useSQL.
  • You have excellent analytical skills and statistical understanding, including experience with: predictive modelling, segmentation, time series analysis and have had hands-on experience in their application.
  • You have had some experience using cloud based analytical platforms such asDatabricks, Snowflake, Google BigQueryetc.
  • You have experience applying machine learning and statistical techniques to solve real-world problems - You can use tools such asTensorFlow, PyTorch, Scikit-learn, and Kerasto build, train, and evaluate various machine learning models such as customer churn prediction, customer lifetime value estimation, customer segmentation, and recommendation systems.



Benefits:

  • VERY Hybrid working environment – 2 days in the office a month
  • Progression opportunities
  • Strong pension and bonus package



Looking for top data and analytics talent? Contact us to learn more about our recruitment services.

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 AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

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

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

The Skills Gap in AI Jobs: What Universities Aren’t Teaching

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.