Technology Risk Assurance – Manager – Financial Services Controls – London or Manchester- Agile Working

CK Search Global
Manchester
1 year ago
Applications closed

Related Jobs

View all jobs

Geospatial Data Scientist

Senior Machine Learning Research Engineer

Lead Machine Learning Engineer, AI

Tech Audit Manager, Vice President – Commercial & Investment Banking Data Management and Artificial Intelligence

Risk Management - Data Scientist Associate

Principal Data Scientist

Our client is a global audit and advisory firm and their technology risk assurance team sits right at the heart of the Audit practice. They leverage deep technical expertise to evaluate the risks associated with the use of technology for businesses in a number of sectors from Consumer Markets, Retail, Technology Media & Telecoms, Natural Resources, Energy & Shipping and Financial Services.They use data analytics procedures to look for trends, anomalies and understand IT and business processes whilst identifying the risks.

Its exciting and interesting work that can include evaluating risks around emerging technologies such as Blockchain, Artificial Intelligence (AI), Robotics Process Automation (RPA) as well as cyber related risks.

The work they do is underpinned by quality. They deliver audits which are trusted and transparent which can be relied upon by companies and their stakeholders.

Youll be comfortable working pro-actively and, managing your own tasks, as well as confident collaborating with others and communicating regularly with Senior Managers, Directors, and Partners. Youll help deliver accurate and transparent reporting to all relevant stakeholders as you provide long term value. 

Youll be someone with:

Experience in providing assurance through re-performance using data analytics (SAS, SQL, Python, VBA, R) Experience in IFRS9 expected credit Loss model review code review, assessing key data risks around assumptions, logic, data sources, PDs, EADs, LGDs and ECL for commercial banks/credit provider. Knowledge on utilising Power Bi data visualisation to create interactive Dashboards for identifying and assessing risks at planning stage of audit. Knowledge of the Financial Services sector specially in regard to revenue and dataflow model surrounding Asset Management, Retail Banking, Capital Markets, Building societies, E-money businesses, Fintech, Debt purchase and payment service providers. Developing high quality efficient audit strategies that are data focused. Interpreting the financial audit impact of IT risks and control weaknesses and Integrating Data Analytics testing into an external audit approach Supporting a team providing on-the job training while also providing data analytics training team through technical support on SAS/SQL/Python coding or other language

Youll be able to be yourself; well recognise and value you for who you are and celebrate and reward your contributions to their business. They are committed to agile working, and we offer everyone the opportunity to work in ways that suit them, their teams, and the task at hand.

They will help you achieve your personal goals and career ambitions, and we have programmes, resources, and frameworks that provide clarity and structure around career development.

Mutual support and respect are core values and they proud of their distinctive, people-centred culture. From informal success conversations to formal mentoring and coaching, well support you at every stage in your career, whatever your personal and professional needs.

Their agile working framework helps us stay connected, bringing teams together where and when it counts so they can share ideas and help one another. At our client youll always have access to the people and resources you need to do your best work.

They know that collaboration is the key to creating value and satisfying experiences at work, so weve invested in state-of-the-art collaboration spaces in our offices. Their people represent a wealth of knowledge and expertise, and well encourage you to build your network, work alongside others, and share your skills and experiences. With a range of multidisciplinary events and dedicated resources, youll never stop learning.

Privacy Policy

Categories:TRA and ConsultingRoles:ManagerLocations:Flexbile on Location London Manchester

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.

How Many AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

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

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

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

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.