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Responsible for selling SentiLink solutions to financial institutions, driving value post-sale, and building long-term relationships with partners.

Lead SentiLink’s go-to-market for crypto, driving enterprise sales across exchanges, wallets, stablecoins, payment platforms, and blockchain providers. Own full sales cycles, define vertical strategy and playbooks, build stakeholder relationships across fraud, risk, compliance, and product, and partner cross-functionally to drive adoption and customer outcomes while monitoring market, regulatory, and fraud trends.

Manage and grow a team of data scientists building end-to-end fraud detection models and products. Provide technical mentorship, lead cross-functional planning and communication, develop production-ready models and code, research new fraud types, and deliver data-driven insights to inform product, operations, and business strategy.

The Growth Account Executive will sell SentiLink solutions to Fintech companies, managing relationships and navigating complex organizations to close deals and ensure partner success.

Design and produce digital and print marketing assets (web pages, emails, ads, collateral, event materials), maintain brand consistency, create web and email templates, collaborate cross-functionally with marketing, sales, and events, manage multiple projects and deadlines, and stay current on design trends and tools to elevate creative output.

As a Senior Applied ML Scientist, you'll develop fraud detection models, research new fraud types, and write production code while collaborating across teams to enhance SentiLink's identity verification solutions.

Lead and grow a model governance team to oversee model validation, monitoring, drift and fair-lending assessments, documentation, change management, and customer/regulatory interactions. Own strategy, customer-facing governance relationships, validation reports, remediation tracking, and hands-on mentorship when needed.

Lead and scale a model governance and quantitative risk team responsible for performance and drift monitoring, validation, documentation, remediation, and customer-facing governance. Set strategy for governance automation and standards, manage cross-functional relationships, prepare reports for customers/regulators, and perform hands-on model review and mentoring as needed.

Design, train, and productionize machine learning models for fraud and identity risk across the full ML lifecycle. Research new fraud types, perform feature engineering and labeling, write production-ready code for real-time decisioning, monitor models, and provide analyses to inform product, sales, and data acquisition.

Build and productionize end-to-end fraud and financial risk models: data acquisition, feature engineering, labeling, training, experimentation, deployment, and monitoring. Research new fraud types, drive product development, support data quality and access, and present analyses to inform product, operations, sales, and marketing.