Data Science Engineering Manager - Audit

Lloyds Bank plc
Bristol
4 days ago
Create job alert

End DateTuesday 02 December 2025Salary Range£0 - £0Flexible Working OptionsHybrid Working, Job ShareJob Description Summary.Job Description****JOB TITLE: Data Science Engineering Manager - AuditSALARY: £71,000 - £100,000 (Dependant on location)LOCATION(S): Bristol/Edinburgh/LondonHOURS: Full-timeWORKING PATTERN: Our work style is hybrid, which involves spending at least two days per week, or 40% of our time, at one of our office sitesAbout this opportunityThis is a multifaceted role within a collaborative team of data analysts, scientists, engineers, and auditors, offering high visibility to senior management and exposure across the Group. The successful candidate will lead the delivery of data science and application development projects. You will design and implement AI-driven solutions that drive innovation and support complex audits within Group Audit & Conduct Investigations. Responsibilities include partnering with auditors, managing stakeholders, mentoring colleagues, and communicating technical concepts clearly. The role requires proficiency in Python, SQL, and PowerBI or Tableau, as well as experience with Google Cloud Platform. You will need strong commitment to developing internal audit and business knowledge, with previous audit or risk experience considered an advantage.Day to day, you will:* Lead multiple data science and application development projects with a high degree of autonomy, leading team members and managing stakeholders.* Design, implement and deliver data pipelines, data models and data science applications in a mixed on-premises and Google Cloud Platform environment.* Apply agile project management and best practices in software development.* Work collaboratively across the audit function to identify innovative opportunities to apply data science techniques for business monitoring, audit planning, and audit delivery.* Support and partner with auditors in the delivery of complex audits applying AI solutions that deliver value.* Communicate on technical topics in plain, simple language that is easy to understand.* Acquire sufficient levels of auditing and business knowledge so that all deliveries are fit for end users’ purpose, positively impact the quality of the department’s assurance work, and improve capabilities.* Answer queries and provide support to end users for our existing tools and applications.* Coach and mentor colleagues on technical skills and support their professional growth.Why Lloyds Banking GroupLike the modern Britain we serve, we’re evolving. Investing billions in our people, data and tech to transform the way we meet the ever-changing needs of our 26 million customers. We’re growing with purpose. Join us on our journey and you will tooWhat you’ll need:* Experience leading application development and data science projects, involving techniques such as graph theory, machine learning, natural language processing, and Generative AI.* The ability to productionise data science models for use by non-technical colleagues, while applying best practices in software development and ensuring that key data science, engineering, and programming concepts are applied.* To be proficient with mainstream data science programming languages and related tools including Python, SQL, and PowerBI or Tableau; be able to review complex code; and be familiar with version control. Previous experience in web application development (e.g. Django, Bootstrap, and jQuery) is an advantage.* Experience with designing and implementing infrastructure on Google Cloud Platform.* Experience managing peers or junior colleagues on projects, holding colleagues accountable, ensuring the quality and timeliness of the project delivery, and fostering a culture of collaboration and continuous improvement.* Coaching, mentoring and feedback skills to support colleagues’ development.* Competence in managing stakeholders, partnering with auditors, and communicating in a way that a non-technical audience can understand.* A strong commitment to developing knowledge and skills in internal auditing. Previous internal audit or risk experience within a financial services environment is an advantage.About working for usOur focus is to ensure we're inclusive every day, building an organisation that reflects modern society and celebrates diversity in all its forms. We want our people to feel that they belong and can be their best, regardless of background, identity or culture. We were one of the first major organisations to set goals on diversity in senior roles, create a menopause health package, and a dedicated Working with Cancer initiative. And it’s why we especially welcome applications from under-represented groups. We’re disability confident. So if you’d like reasonable adjustments to be made to our recruitment processes, just let us knowWe also offer a wide-ranging benefits package, which includes:• A generous pension contribution of up to 15%• An annual performance-related bonus• Share schemes including free shares• Benefits you can adapt to your lifestyle, such as discounted shopping• 30 days’ holiday, with bank holidays on top• A range of wellbeing initiatives and generous parental leave policies**At Lloyds Banking Group, we're driven by a clear purpose; to help Britain prosper. Across the Group, our colleagues are focused on making a difference to customers, businesses and communities. With us you'll have a key role to play in shaping the financial services of the future, whilst the scale and reach of our Group means you'll have many opportunities to learn, grow and develop.****We keep your data safe. So, we'll only ever ask you to provide confidential or sensitive information once you have formally been invited along to an interview or accepted a verbal offer to join us which is when we run our background checks. We'll always explain what we need and why, with any request coming from a trusted Lloyds Banking Group person.****We're focused on creating a values-led culture and are committed to building a workforce which reflects the diversity of the customers and communities we serve. Together we’re building a truly inclusive workplace where all of our colleagues have the opportunity to make a real difference.**With 320 years under our belt, we're used to change, and today is no different. Join us and help drive this change, shaping the future of finance whilst working at pace to deliver for our customers.Here, you'll do the best work of your career. Your impact will be amplified by our scale as you learn and develop, gaining skills for the future.
#J-18808-Ljbffr

Related Jobs

View all jobs

AI-Driven Data Science Engineering Manager, Audit

Senior Data Science Manager, Business Banking New Cardiff, London or Remote (UK)

Contract Data Engineering Manager

Senior Data Engineering Manager

Genetic Data Engineering Manager - Scale & Lead Pipelines

Wealth Data Engineering Manager – AI-Ready Platforms (VP)

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 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.

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.

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.