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The role involves leading security initiatives, building security tools, conducting vulnerability assessments, and collaborating with teams to enhance cloud security and risk management.

The Enterprise Account Executive will drive SentiLink's healthcare market growth by managing complex sales cycles, developing strategy, building relationships, and partnering cross-functionally.

Lead design, build, and scaling of end-to-end learning programs across the employee lifecycle. Define L&D strategy, partner with business leaders, design and facilitate trainings/workshops, create scalable knowledge resources, measure program effectiveness, and execute high-impact initiatives to develop managers and leaders.

Lead and scale strategic enterprise sales to win large, multi-stakeholder customers and expand into new industries. Build and coach a high-performing strategic sales team, recruit lighthouse customers, drive go-to-market strategy, collaborate with Product/Marketing/Partnerships, influence product roadmap, and represent SentiLink at executive briefings and industry events to create repeatable, scalable sales motions.

Lead and execute SentiLink’s B2B growth marketing to generate qualified pipeline and ARR. Build integrated campaigns, ABM, SEO/AEO, website conversion, events, and analytics. Partner with sales, product, and data teams; recruit and lead a team; measure marketing by pipeline and revenue.

Lead and grow a model risk and governance team, set strategy, and run model validation, performance and drift monitoring, fair-lending assessments, governance documentation, model inventory, and change management. Own customer and regulator-facing governance relationships, prepare validation reports, track remediation, balance strategic roadmap with operational demands, and perform hands-on technical work as needed.

Build, train, and productionize machine learning models for fraud detection and identity verification. Perform data acquisition, feature engineering, experimentation, monitoring, and analyses to inform product, sales, and risk decisions. Collaborate with engineering, data acquisitions, and risk operations to maintain data quality and ship production-ready code. Research new fraud types and contribute to new product development across the ML lifecycle.

Design, develop, and productionize machine learning models for fraud detection and identity verification. Perform end-to-end model lifecycle work including data acquisition, feature engineering, training, experimentation, deployment, and monitoring. Research new fraud types, build product-focused models, write production-ready code, and collaborate with engineering, operations, and data teams to support product and business needs.

Build and deploy production ML models for fraud and identity risk across the full ML lifecycle. Research new fraud types, engineer features, write production-ready code, collaborate with cross-functional teams, and inform product, data acquisition, and business decisions.

Build and productionize machine learning models for fraud and identity risk across the full ML lifecycle: data acquisition, feature engineering, training, deployment, monitoring, and analysis. Research new fraud types, collaborate cross-functionally, write production-ready code, and inform product, sales, and data acquisition decisions.