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About the role
Stripe is hiring a Specialist Solutions Architect for its Radar fraud and risk management platform, covering the APAC region from Singapore. You will work with sales and pre-sales teams as a domain expert on fraud prevention, helping customers and internal teams get the most out of Radar. This role suits someone with deep fraud and risk experience who enjoys advising customers and shaping product direction.
What you'll do
Serve as a subject matter expert on the Radar product suite to help close opportunities worldwide
Track and share trends in fraud and risk management, including AI detection, processor-agnostic setups, and the move toward holistic business risk
Build expertise in fraud system architecture, covering rule management, machine learning tuning, and manual review optimisation
Understand the risk and identity integrations Stripe needs to support, such as device fingerprinting, identity verification, bot mitigation, and case management
Define and share best practices and reusable assets with the wider go-to-market organisation
Create reference architectures and refine reference solutions based on product updates and customer feedback
Partner with the Radar product sales team on large, complex opportunities, leading solution strategy and owning key customer relationships
Work closely with the Radar product team to get early exposure to new products and features
Act as a voice of the customer internally to prioritise product and feature requests, sharing insights on competitors and emerging fraud threats
Escalate and drive complex technical issues to resolution with Stripe's engineering team
Keep up with competitive analysis and market differentiation
Support marketing events such as executive briefings, conferences, user groups, and trade shows
Good to know
The role is based in Singapore and involves communicating with customers in the Greater China region
Requirements
7+ years in pre-sales, technical consulting, product, or risk strategy focused on e-commerce fraud prevention, risk management, or trust and safety
Deep working knowledge of the fraud and risk ecosystem, including payment fraud (CNP), account takeover, first-party misuse, and policy or content abuse
Knowledge of risk management architectures, including rules engines, machine learning models, and manual review queues
Knowledge of fraud detection signals and data such as device and IP data, behavioural biometrics, identity verification, and graph-based analysis
Strong knowledge of software engineering and architecture patterns and how different technologies and systems interact
Excellent communication, presentation, and interpersonal skills, able to explain complex concepts to technical and non-technical audiences
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