Data Analyst

Cramond Bridge
10 months ago
Applications closed

Related Jobs

View all jobs

Data Analyst

Data Analyst

Data Analyst

Data Analyst

Data Analyst

Data Analyst

Join us as a Data Analyst

Take on a challenge in the RBS International Data Management team, in which you’ll contribute to the analysis of RBS International business outcomes and key data elements (KDEs) to identify data quality issues, as well as business issues related to people, platform and processes

We’ll look to you to manage RBS International's  business outcomes, provide high quality analytical input to help develop and implement innovative data profiling solutions, processes and resolve problems across the bank

This is a hands on role in which you'll hone your statistical and analytical data analysis expertise and gain valuable experience in a dynamic area of our business

What you'll do

As a Data Analyst, you'll play a key role in supporting the delivery of high quality business solutions. You’ll be performing data extraction, manipulation, processing and analysis, using the RBS International Amazon Web Services (AWS) Cloud based data quality detection engine and bank data profiling solutions, alongside developing and performing standard queries to ensure data quality and identify data inconsistencies and missing data.

Day-to-day, you’ll also be:

Managing RBS International's business outcomes

Collecting, profiling and mapping appropriate data to use in our AWS Cloud based data profiling solution as well as for ongoing data activities

Maintaining and developing the RBS International AWS DQ Detection Engine Business Rules and Rules Repository used for data profiling  

Helping to develop Tableau dashboards to present statistical and analytical data quality results to Executive Data Owners (EDOs)

Working with other RBS International business areas in the identifying and documenting of data migration paths and processes, standardising KDE naming, data definitions, modelling and attending the NatWest Glossary Working Group

Helping to interpret customer needs and identifying operational risk issues, turning them into functional or data requirements and process models

Building and maintaining collaborative partnerships with key business stakeholders, including data domain leads, EDOs and EDO delegates

The skills you'll need

We’re looking for someone with experience of using data analysis tools and delivering data insights within a technology, data management, or data analytics function.

Detailed knowledge and evidence of application of, AWS, Structured Language Query (SQL), and JavaScript Object Notation (JSON) is an absolute requirement for this role.

We’ll also look for:

An in-depth understanding of the interrelationships of data and multiple data domains

A background in delivering research based on qualitative and quantitative data across a range of subjects

Excellent communication and influencing skills

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.

Careers Jobs

Machine Learning Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Are you considering a career change into machine learning in your 30s, 40s or 50s? You’re not alone. In the UK, organisations across industries such as finance, healthcare, retail, government & technology are investing in machine learning to improve decisions, automate processes & unlock new insights. But with all the hype, it can be hard to tell which roles are real job opportunities and which are just buzzwords. This article gives you a practical, UK-focused reality check: which machine learning roles truly exist, what skills employers really hire for, how long retraining realistically takes, how to position your experience and whether age matters in your favour or not. Whether you come from analytics, engineering, operations, research, compliance or business strategy, there is a credible route into machine learning if you approach it strategically.

Jobs

How to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.

Education Jobs

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.