Finance Data Analyst

London
1 year ago
Applications closed

Related Jobs

View all jobs

Finance Data Analyst

Finance Data Analyst

Finance Data Analyst

Finance / Data Analyst

Finance Data Analyst (Hybrid) — Power BI, SQL & Excel

Finance Data Analyst (SAP S/4HANA) – Hybrid Role

Finance Data Analyst

Up to £58,000

Hybrid - Central London - 2x per week

We are representing a B2B professional services business who are looking for a Finance Data Analyst to join their organisation. This is a global business, headquartered in the UK, with a great history and reputation in the market. This role will report to the Finance Manager and will join an established CFO function of approximately 15 people.

As the Finance Analyst, you will be work on a mixture of reporting, financial model development and supporting their finance system (Infor Sun Systems). You will develop financial models using Excel, produce and analyse monthly management information, and develop interfaces between the finance system and other systems - e.g. expenses, timesheet recording etc.

The company are looking to upgrade their ERP system in future, as well different finance applications, so there will be the opportunity to be a part of several important projects focused on bettering the business.

We are looking for:

Advanced reporting and modelling skills with Excel - e.g. VLOOKUP, PivotTables, VBA, and Macros
Prior experience working within Finance teams
Strong communication and interpersonal skills

It would be a bonus if you had:

Previous experience with Infor Sun Systems software
Experience with Visio Data Visualizer add-in for Excel
An accountancy background or relevant qualification/training

If this role sounds of interest, please apply today

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.

Jobs

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

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

Careers Jobs

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.

Education

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

Machine learning has moved from academic research into the core of modern business. From recommendation engines and fraud detection to medical imaging, autonomous systems and language models, machine learning now underpins many of the UK’s most critical technologies. Universities have responded quickly. Machine learning modules are now standard in computer science degrees, specialist MSc programmes have proliferated, and online courses promise to fast-track careers in the field. And yet, despite this growth in education, UK employers consistently report the same problem: Many candidates with machine learning qualifications are not job-ready. Roles remain open for months. Interview processes filter out large numbers of applicants. Graduates with strong theoretical knowledge struggle when faced with practical tasks. The issue is not intelligence or effort. It is a persistent skills gap between university-level machine learning education and real-world machine learning jobs. This article explores that gap in depth: what universities teach well, what they routinely miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in machine learning.

🍪 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.