Treasury Liquidity Analytics Manager

Empirical Search

London

1 year ago

Applications closed

Related Jobs

View all jobs

SQL Data Engineer

Role Description

F&L analytics production including liquidity metrics, investment compositions, variance analysis, trending, horizon scanning and presentation to key stakeholders Use and development of data and analytical tools to model how liquidity and funding impacts the Group, exploring better ways of presenting information, handling data, identifying trends, absorbing and developing new concepts, and drawing conclusions. Translating acquired technical knowledge into analysis and ideas Real-time decision making on business F&L risk management (Retail and CB), senior manager escalation, and analytics development Engaging with key stakeholders including Group Risk, other Treasury Markets desks and facilitating discussion/driving action at F&L Forums with key peers

Role Requirements

Strong analytical and data skills, with an ability to absorb concepts, problem solve and develop solutions An interest in data analytics both in terms of handling data, manipulating it, and drawing conclusions. Use of statistical techniques and experience in SQL, VBA, Power BI/Tableau an advantage albeit not a pre-requisite A real interest in Treasury and the principles behind F&L and what drives them as well as understanding of the balance sheet/key businesses and the F&L risk inherent within them Strong communication skills. The ability to break down and explain technical information and concepts clearly and thoughtfully An ability to collaborate and pro-actively engage with colleagues within the team & key stakeholders across the Group Able to prioritise and manage time optimally

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.