Fraud Product Owner

Manchester
3 days ago
Create job alert

Join us as a Fraud Product Owner

Build your career as an influential fraud data and analytics leader and evangelist

You’ll lead a cross functional team of data scientists, engineers, analysts and technologists to deliver impactful and innovative change projects transforming the way in which we protect our customers and the bank from fraudsters

This is a chance to collaborate with wider business and peers to deliver on the joint fraud and bank vision and help transform and deliver our team’s culture

What you'll do

In this role, you’ll bring the point of view of our customers, end users or stakeholders to the forefront of understanding the product vision set by you and the bank. With exceptional leadership skills, you’ll drive your team towards this vision, regularly stopping along the way to check-in, adjust, calibrate and move forward.

We’ll also look to you to make sure that stories and enablers meet acceptance and quality criteria, keeping them in-line with the vision, features and programme increment objectives.

Your responsibilities include:

Setting a vision, strategy and roadmap for your team, driving delivery against commercial and business targets maximising return on investment

Leading and supporting the development of your feature teams by prioritising features, answering questions and removing blockers

Making sure that the backlog is maintained, and that your delivery teams are frequently collaborating with customers or users to populate and refine the backlog

Helping to drive programme iteration objectives at a team or enterprise level, and coordinating with other product owners and system teams

Owning a product vision and roadmap, inspiring and motivating your team to deliver cutting edge, data driven fraud prevention solutions

Tracking and reporting progress, and attending any retrospective, spanning all delivery teams that are involved with delivering the product

The skills you'll need

We’re looking for a creative thinker, with a good understanding of Agile methodologies and experience of working in an Agile team. You’ll need to be able to relate your everyday work to the broader strategic vision set for your feature team, along with the ability to maintain a strong focus on business outcomes. And, you’ll have excellent communication and influencing skills.

You’ll also need:

Knowledge or experience of working with modern cloud technologies, streaming pipelines and data science platforms

Experience leading product deliveries from concept to production across data and fraud domains such as orchestration systems, data science models and data lakes

The ability to convey complex technical topics to a non-technical audience through storytelling

Commercial and business acumen to drive an entrepreneurial mindset within the team

Experience of changing team or department mind-sets, culture and structure whilst maintaining strong drive and motivation

Related Jobs

View all jobs

Fraud Product Owner

Fraud Product Owner

Fraud Product Owner

Senior Ad Tech Engineer

Transaction Banking and Other - London - Vice President - Software Engineering London, Greater [...] (Basé à London)

Software Engineer II - Machine Learning

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Machine‑Learning Jobs for Non‑Technical Professionals: Where Do You Fit In?

The Model Needs More Than Math When ChatGPT went viral and London start‑ups raised seed rounds around “foundation models,” many professionals asked, “Do I need to learn PyTorch to work in machine learning?” The answer is no. According to the Turing Institute’s UK ML Industry Survey 2024, 39 % of advertised ML roles focus on strategy, compliance, product or operations rather than writing code. As models move from proof‑of‑concept to production, demand surges for specialists who translate algorithms into business value, manage risk and drive adoption. This guide reveals the fastest‑growing non‑coding ML roles, the transferable skills you may already have, real transition stories and a 90‑day action plan—no gradient descent necessary.

Quantexa Machine‑Learning Jobs in 2025: Your Complete UK Guide to Joining the Decision‑Intelligence Revolution

Money‑laundering rings, sanctioned entities, synthetic identities—complex risks hide in plain sight inside data. Quantexa, a London‑born scale‑up now valued at US $2.2 bn (Series F, August 2024), solves that problem with contextual decision‑intelligence (DI): graph analytics, entity resolution and machine learning stitched into a single platform. Banks, insurers, telecoms and governments from HSBC to HMRC use Quantexa to spot fraud, combat financial crime and optimise customer engagement. With the launch of Quantexa AI Studio in February 2025—bringing generative AI co‑pilots and large‑scale Graph Neural Networks (GNNs) to the platform—the company is hiring at record pace. The Quantexa careers portal lists 450+ open roles worldwide, over 220 in the UK across data science, software engineering, ML Ops and client delivery. Whether you are a graduate data scientist fluent in Python, a Scala veteran who loves Spark or a solutions architect who can turn messy data into knowledge graphs, this guide explains how to land a Quantexa machine‑learning job in 2025.

Machine Learning vs. Deep Learning vs. MLOps Jobs: Which Path Should You Choose?

Machine Learning (ML) continues to transform how businesses operate, from personalised product recommendations to automated fraud detection. As ML adoption accelerates in nearly every industry—finance, healthcare, retail, automotive, and beyond—the demand for professionals with specialised ML skills is surging. Yet as you browse Machine Learning jobs on www.machinelearningjobs.co.uk, you may encounter multiple sub-disciplines, such as Deep Learning and MLOps. Each of these fields offers unique challenges, requires a distinct skill set, and can lead to a rewarding career path. So how do Machine Learning, Deep Learning, and MLOps differ? And which area best aligns with your talents and aspirations? This comprehensive guide will define each field, highlight overlaps and differences, discuss salary ranges and typical responsibilities, and explore real-world examples. By the end, you’ll have a clearer vision of which career track suits you—whether you prefer building foundational ML models, pushing the boundaries of neural network performance, or orchestrating robust ML pipelines at scale.