Senior Administrator - Tax

HAYS
London
10 months ago
Applications closed

Related Jobs

View all jobs

Senior Data Engineer (DATABRICKS)

Senior Data Engineer

Funds Technology – Data Analyst Manager Assistant Manager Senior Consultant

Data Analyst

Senior Data Engineer - Scalable Pipelines & Insights

Senior Data Engineer - Platforms and Tooling

Permanent Role - Accountancy Firm - Senior Admin (Tax Team) - Farringdon

Your new company 
An established accountancy firm based in the heart of London is seeking a Senior Administrator to join their busy Tax team!
Your new role 

Drafting and preparing engagement and disengagement letters Conducting mail merges  Administration support to include creating reports, data analysts and WIP management  Filing and scanning documents on an e-filing system and maintaining records Overseeing the office annual leave calendar ensuring there is enough cover Organising lunches and events for the team Completing KYC processes including Companies House checks Onboarding new clients on CCH and HMRC Expenses management  Booking travel and accomodation when required 

What you'll need to succeed 
Previous admin experience in a similar role within a professional services firmAdvanced skills in MS Office, including Outlook, Excel and PowerPointTime managementAbility to converse with all levels of seniorityAbility to travel into the office 5 days a weekWhat you need to do now 
If you're interested in this role, click 'apply now' to forward an up-to-date copy of your CV.
# 4665294

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.

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.

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.

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.