Audit Data Analytics Manager

MHA
City of London
3 months ago
Applications closed

Related Jobs

View all jobs

Audit Manager - Data Science. R00AOR05263

Data Analytics, Strategic testing - Bournemouth/ London

People Data Governance Manager

People Data Governance Manager

People Data Governance Manager

Data Analyst - Product

Job Title: Audit Data Analytics Manager

Office: Flexible to be based at any of our office locations (Hybrid)

Competitive salary, negotiable depending on your experience and expertise.


Are you ready to bring your Audit Data Analytics expertise to a role where your skills and unique perspective can make a lasting impact?


What Sets Us Apart

At MHA, we’re about people first – our people, our clients, and the communities we serve. As one of the UK’s top 15 accountancy firms and a proud member of Baker Tilly International, the 9th largest network in the world, we are a hub of global knowledge with a strong local commitment.

Our people-focused approach truly sets us apart. Here, you won’t just be another face in the firm; you’ll be part of an ambitious, driven team dedicated to continuous learning and growth. We invest in our people because they are the foundation of our success. With access to unparalleled resources and award-winning development programmes, you’ll have the freedom to shape your career while making a meaningful impact. We take immense pride in being awarded gold for Investors in People. This recognition reflects our deep commitment to nurturing talent and ensuring every individual feels valued and supported.


Here, You’ll Go Beyond Numbers

We’re looking for a talented Audit Data Analytics manager who thrives in a dynamic environment and embraces the chance to tackle complex challenges. At MHA, you’ll be surrounded by passionate professionals who are dedicated to making a difference. Your work in Audit will go beyond the numbers – it’s about building meaningful client relationships, understanding their businesses, navigating risks, and delivering insights that drive real results.

  • Delivers hands-on data analytics expertise and solutions to engagement teams, enabling more effective and efficient audits
  • Builds data analytics capabilities across the entire audit practice through structured learning programmes, resources and continuous skills development.
  • Embeds data analytics techniques into standard audit methodology to ensure consistent application, regulatory compliance and quality outcomes.
  • Research, designs and implements AI solutions that addresses non-judgemental audit procedures and routines.
  • Creates and maintains bespoke data analytics tools and workflows that enhance audit quality, improve efficiency and address firm-specific requirements.
  • Drives the identification, assessment and implementation of emerging technologies and approaches to transform audit quality and differentiate the firm's service offering.
  • Establishes and maintains the frameworks, controls and processes that ensure data analytics work meets professional standards and regulatory expectations.


What We’re Looking For

  • Fully qualified ACA/ACCA with a minimum of 2-3 years’ post qualified experience in a technical role, external audit or equivalent.
  • Relevant recent experience with audit data analytics software and an awareness of current developments.
  • Proficiency in low-code development platforms
  • Expert-level skills in Power BI or similar data visualization tools
  • Strong understanding of audit processes and methodologies
  • Excellent problem-solving and analytical skills
  • Ability to communicate complex technical concepts to non-technical audiences
  • Demonstrable ability to work well within a team and on your own.
  • Influencing – the ability to persuade others of your point of view.
  • Knowledge of machine learning and AI applications in audit



Rewards That Resonate

You’re more than a number to us; you’re an individual with unique talents and aspirations. Our benefits are designed to support your well-being, foster your professional growth, and recognise your contributions.

  • Agile Working: Enjoy the flexibility of core hours from 10 AM to 2 PM and two home working days, allowing you to balance your work and personal commitments seamlessly.
  • 25 days holiday Plus Bank Holidays, plus the opportunity to buy or sell up to 5 days
  • Competitive salary package
  • Employee recognition awards: Outstanding Performance Award Bonus and other recognition initiatives.
  • New and improved programme for succession planning and supportive management structure to help you realise your potential
  • Employee Assistance Programme: Access a free confidential 24-hour support service, including unlimited counselling sessions and virtual doctors available for you and your family.
  • And lots more!



Are You Ready to Elevate Your Career?

Apply now and be part of a team that celebrates diversity, champions innovation, and prioritises your success!

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

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.

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.

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.

🍪 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.