Analytical Consultant

Cramond Bridge

10 months ago

Applications closed

Related Jobs

View all jobs

Data Analyst Consultant

Senior Data Analyst & Consultant — Remote & Impact

Senior Consultant - Health Economist/Statistician

Senior Consultant - Data Analyst

Implementation Consultant – Data Analytics & Optimization

Data Analytics Consultant – Strategy & Insights

Join us as an Analytical Consultant

For someone with a background in explaining business performance and recommending management actions through expert use of data analytics in a client facing role, this is a valuable opportunity to help deliver crucial business advice within our franchise-wide team

We’ll look to you to develop processes that enable the distribution and understanding of insights into the wider bank

You’ll be supporting the business by using insights to drive effective decision making, giving you excellent recognition and the chance to raise your profile

What you'll do

This key role will see you helping to build and deliver analytics and insights for the franchise by creating and leveraging all financial and business performance including balance sheet and P&L. You’ll be developing and maintaining effective statistical profitability models and associated analytics, while providing actionable MI on all aspects of model performance.

As well as this, you’ll be:

Providing insight through analysis and communicating this effectively to your stakeholders

Identifying opportunities as they arise for business strategy improvement through a range of levers such as product, propositional, target operating model, and pricing

Identifying opportunities for improvement, both in terms of the analysis being produced, and the approaches and processes used within the team

Working with the team manager and other managers to maximise team performance and effectiveness, sharing your technical expertise to improve team capability

The skills you'll need

We’re looking for a keen problem solver who’s qualified to degree level in a numerate discipline and has data driven analysis and consulting skills. Along with extensive banking or financial services experience, you’ll have knowledge of key analytical and visualisation software. You’ll need consultancy skills, but a consultancy background is not essential.

You’ll also need:

Experience in analytical and data science including SQL, Python, and Tableau or an equivalent

An understanding of stakeholder products, propositions, customers, customer journeys, markets, and competitors

Broad experience of risk and finance systems, methodologies, and processes in a retail or wholesale banking environment

An understanding of the regulatory regime and risk management and control processes applicable to businesses within the financial services industry

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.

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.

Feb 3, 2026

Jobs

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.

Jan 23, 2026

Education

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.