Senior Fraud Analyst

Smartnumbers

full-time

Posted on: 9/3/2025

Origin: • 🇬🇧 United Kingdom

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💰 £75,000 per year

Senior

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About the role

Senior Fraud Analyst to join the Product & Success - Analytics team, reporting to the Chief Product & Success Officer.
Use Smartnumbers solutions to support delivery of value to customers and investigate suspicious events.
Collaborate with customers throughout the relationship lifecycle and act as their champion within the organisation.
Articulate measurable value to customers aligned with industry and regulatory metrics.
Operate on the Smartnumbers platform supporting organisations across financial services, telecommunications, and others.
Hands-on, customer-facing and multi-team-focused role influencing product roadmap and industry perspectives.
Analytics responsibilities: monitor, analyse, interpret fraud patterns; monitor industry trends; generate insights; develop KPIs; perform strategic reviews; support automation of analytics.
Customer engagement: present findings to customers; create compelling narratives; participate in industry forums; support adoption of new capabilities.
Cross-team collaboration: support sales and marketing reports; provide insights to Product team from customer feedback and analysis.

Curious - You want to understand the problems our customers face and how we might solve them.
Eye for detail and drive to understand complex business logic and standardise processes.
Experience developing and implementing fraud detection rules utilising machine learning and anomaly detection.
Customer-centric - start with the customer and work back while understanding business constraints.
Clear communicator capable of explaining things to diverse audiences.
Natural ability to collaborate across teams and communicate complex findings.
Proven track record of defining and championing outcome-based metrics that drive customer success.
Comprehensive experience working with data in a product environment, particularly in authentication, fraud or risk analytics.
Strong technical foundation in SQL and Python/R, with ability to wrangle complex datasets.
Experience with modern data visualisation tools (Looker, Tableau, Power BI).
Bachelor's degree or relevant qualifications in a quantitative field (Data Analytics, Statistics, Computer Science, or similar).
Enthusiasm for rapid experimentation and a data-driven approach.
Understanding of Agile methodologies and how they apply to data analytics.
This isn’t a checklist; strong desire to learn fast, get stuck, and deliver value is essential.