Fraud Product Owner

Chalkwell
1 day 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

Payment Compliance Lead

Senior Software Engineer (AI)

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 Leadership for Managers: Strategies to Motivate, Mentor, and Set Realistic Goals in Data-Driven Teams

Machine learning (ML) has become an indispensable force in the modern business world, influencing everything from targeted marketing campaigns to advanced medical diagnostics. As industries integrate predictive algorithms and data-driven decision-making into their core operations, the need for effective leadership in machine learning environments has never been greater. Whether you’re overseeing a small team of data scientists or spearheading an enterprise-scale ML project, your leadership style must accommodate rapid innovation, complex problem-solving, and diverse stakeholder expectations. This guide provides actionable insights into how you can motivate, mentor, and establish achievable goals for your machine learning teams—ensuring they thrive in data-driven environments.

Top 10 Books to Advance Your Machine Learning Career in the UK

Machine learning (ML) remains one of the fastest-growing fields within technology, reshaping industries across the UK from finance and healthcare to e-commerce, telecommunications, and beyond. With increasing demand for ML specialists, job seekers who continually update their knowledge and skills hold a significant advantage. In this article, we've curated ten essential books every machine learning professional or aspiring ML engineer in the UK should read. Covering foundational theory, practical implementations, advanced techniques, and industry trends, these resources will equip you to excel in your machine learning career.

Navigating Machine Learning Career Fairs Like a Pro: Preparing Your Pitch, Questions to Ask, and Follow-Up Strategies to Stand Out

Machine learning (ML) has swiftly become one of the most in-demand skill areas across industries, with companies leveraging predictive models and data-driven insights to solve challenges in healthcare, finance, retail, manufacturing, and beyond. Whether you’re an early-career data scientist aiming to break into ML, a seasoned engineer branching into deep learning, or a product manager exploring AI-driven solutions, machine learning career fairs offer a powerful route to connect with prospective employers face-to-face. Attending these events can help you: Network with hiring managers and technical leads who make direct recruitment decisions. Gain insider insights on the latest ML trends and tools. Learn about emerging job roles and new industry verticals adopting machine learning. Showcase your interpersonal and communication skills, both of which are increasingly important in collaborative AI/ML environments. However, with many applicants vying for attention in a bustling hall, standing out isn’t always easy. In this detailed guide, we’ll walk you through how to prepare meticulously, pitch yourself confidently, ask relevant questions, and follow up effectively to land the machine learning opportunity that aligns with your ambitions.