Product Manager for AI at GTreasury driving AI/ML integration in treasury operations. Leading product vision for cash forecasting, liquidity optimization, and risk analytics.
Responsibilities
Define and deliver the AI/ML vision and roadmap across cash forecasting, liquidity optimization, fraud detection, payment intelligence, and risk analytics
Identify high-impact opportunities to apply AI with clear customer ROI using user research, data analysis, and market insights
Partner with AI engineers and data scientists to shape model behavior, evaluate performance, and guide implementation decisions
Translate complex data science capabilities into intuitive product experiences that enhance (not replace) treasury workflows
Champion accuracy, explainability, compliance, and operational confidence in every AI feature
Lead agile delivery with design and engineering, maintaining a clear and prioritized backlog for AI-powered features
Act as the voice of the customer, ensuring AI solutions address real treasury needs and integrate seamlessly into existing workflows
Work with product peers to embed AI capabilities across the GTreasury platform (payments, cash management, risk, etc.)
Monitor emerging AI technologies, regulatory considerations (AI governance, bias, transparency), and competitive landscape
Define success metrics for AI features and continuously optimize based on model performance, user feedback, and business impact
Create internal and external enablement materials that build confidence in AI-driven insights and recommendations
Requirements
5+ years in product management, ideally in SaaS, fintech, or enterprise platforms
Experience working on data-driven or AI-enhanced products with demonstrated understanding of ML concepts, predictive modeling, and analytics
Strong knowledge of treasury operations including cash management, forecasting, payments, liquidity, and risk management
Proven ability to launch and scale new products or AI-powered features from concept to adoption
Experience collaborating closely with data scientists, ML engineers, and AI teams
Agile development familiarity and comfort working in fast-paced, global environments
Ability to evaluate AI model performance, understand limitations, and make informed product trade-offs
High empathy for users paired with strong analytical and decision-making skills
Excellent communication skills — can explain AI capabilities and limitations to both technical and non-technical audiences.
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