Lead Product Manager – AI & Trust Intelligence

Define and own the product strategy for AI and Trust Intelligence products including trust scores risk signals decisioning models model outputs reason codes and customer-facing insights.

Build a cohesive multi-year product roadmap aligned to business priorities carrier data capabilities enterprise customer needs fraud trends and evolving regulatory expectations.

Drive the vision for next-generation mobile identity intelligence including AI-powered fraud detection adaptive risk scoring identity reputation signal intelligence and real-time trust decisioning.

Lead the development of AI/ML-powered trust data and intelligence products supporting orchestration across signals rules and models.

Productize AI models into practical enterprise offerings including APIs batch outputs dashboards reports decision engines and workflow integrations.

Define customer-facing score interpretation reason codes confidence indicators and model explainability requirements.

Co-establish product metrics for model performance and customer value including precision recall false positive rates lift stability drift coverage conversion impact fraud reduction and operational efficiency with Data and AI partners.

Build processes for continuous model improvement using customer outcomes feedback data fraud signals and partner insights.

Lead discovery sessions that translate customer problems into AI product opportunities pilot designs and measurable success criteria.

Design proof-of-value programs that demonstrate the impact of AI and trust intelligence data products.

Partner with GTM Sales and Solutions Engineering to support enterprise adoption customer success and product-led growth.

Develop clear product narratives demos use case playbooks and technical explanations that make AI outputs understandable and actionable for business fraud risk and technology stakeholders.

Work with carrier partners and strategic data providers to identify opportunities to deepen signal coverage data quality and product differentiation.

Ensure AI product capabilities are explainable auditable measurable and appropriate for high-trust enterprise environments.

Partner with privacy legal security data governance and AI governance teams to ensure AI capabilities are developed with privacy-by-design and secure-by-design principles.

Maintain alignment with applicable privacy identity fraud and risk management expectations including PIPEDA data governance requirements AI governance customer contractual obligations and emerging responsible AI practices.

6 to 8 years of product management experience in AI/ML products data products fraud/risk digital identity cybersecurity fintech telecom enterprise SaaS or decisioning platforms.

Experience working closely with data science machine learning data engineering software engineering architecture privacy and security teams.

Experience taking AI analytics scoring or data products from discovery through production launch and ongoing optimization.

Familiarity with model development workflows including feature engineering labels training data evaluation metrics monitoring feedback loops and model governance.

Experience with fraud prevention identity verification digital onboarding authentication account takeover synthetic identity scams KYC AML or transaction monitoring is strongly preferred.

Experience with APIs data platforms scoring engines analytics dashboards decisioning systems or customer-facing data products.

Strong product instincts and ability to balance customer needs AI capability privacy expectations security requirements and user experience.

Ability to translate complex AI data and technical concepts into clear business outcomes for executives customers and cross-functional teams.

Strong understanding of AI/ML product concepts including model performance explainability precision/recall false positives drift confidence scores and human-in-the-loop workflows.

Strong analytical skills and comfort working with data metrics APIs risk signals product analytics and customer outcome measurement.

Familiarity with privacy-by-design secure-by-design responsible AI and enterprise data governance principles.

Customer-facing confidence with the ability to support sales pilots executive briefings technical discovery and product demos.