• Refactor Neural Network Collaborate with architect and author of neural network bond risk product to identify areas for improvement. • Lead architecture and development effort Ongoing Contribute to the design, development, and deployment of firm-wide architecture, norms, policies, infrastructure and methodologies for machine learning activities across multiple company groups. • Design, develop, and deploy machine learning models into production environments. • Collaborate with data scientists to translate prototypes into production-ready systems. • Build and maintain data pipelines, feature stores, and model-serving infrastructure. • Evaluate and optimize model performance, latency, and scalability. • Implement automated training, testing, and deployment workflows (MLOps). • Monitor models in production and address issues related to drift, performance degradation, or data quality. • Conduct code reviews and ensure best practices in ML engineering and software development. • Stay current with emerging ML/AI technologies and recommend tools or frameworks that improve team efficiency.

• Design, build, and maintain scalable data pipelines and workflows to support analytics, fraud detection, model development, and ongoing data monitoring (e.g., using Spark, Airflow, or similar distributed systems). • Leverage and build agentic AI and LLM-powered systems to automate data exploration, anomaly detection, vendor evaluation, and investigative workflows, increasing the speed and depth of insight generation. • Build and optimize models using a variety of input data types, including tabular data, natural language, point clouds, and images, in support of fraud detection and identity verification use cases. • Own data quality and integrity for critical datasets, implementing monitoring, validation checks, and anomaly detection to ensure reliable input to models and downstream decision systems. • Take ownership of project outcomes from scoping through delivery, managing data quality, technical trade-offs, and timelines; proactively escalate risks and work cross-functionally to resolve challenges. • Evaluate and integrate third-party data vendors and external datasets, including designing experiments to assess data quality, coverage, lift, and long-term value for Socure’s models and products. • Collaborate closely with Product, Engineering, and Risk teams to define data requirements, shape roadmap priorities, and deliver insights that guide strategic decisions for fraud and identity products. • Conduct in-depth research to explore new data sources and develop novel algorithms and features that advance the state of the art in fraud detection, identity resolution, and risk scoring. • Lead the end-to-end ML/analytics lifecycle for assigned projects: problem definition, data exploration, feature engineering, modeling, evaluation, deployment handoff, and post-deployment monitoring where applicable. • Present findings, trade-offs, and recommendations to technical and executive stakeholders with clarity and influence, adapting communication for audiences ranging from engineers to non-technical business leaders. • Mentor and share knowledge with peers and junior data scientists, fostering a culture of experimentation, rapid iteration, and continuous learning aligned to Socure’s leadership competencies. • Stay current with advancements in AI, machine learning, and data infrastructure (including LLMs and agentic frameworks), and apply innovative techniques to real-world fraud and identity problems. • Model Socure’s embedded leadership competencies in day-to-day work: continuous learning, effective communication, accountability, team development, decision making, and managing change.

• Champion a data-first approach across internal teams and client engagements, promoting clarity and impact • Build and deploy machine learning models to prevent fraud across diverse fintech use cases • Use data and models to support the development of risk mitigation strategies and interventions while preserving and improving the user experience • Work directly with clients to understand challenges and deliver high-impact, data-driven solutions • Evolve our risk metrics, the supporting datasets, and how we measure the causal impact of initiatives • Collaborate with engineering to scale models into production and optimize performance

• Principal technical expert for how Geisinger evaluates, monitors, and optimizes AI systems • Set technical direction for AI evaluation across a growing portfolio • Provide technical leadership to a team of data analysts • Partner with AI program teams to improve validation and monitoring methods • Design validation studies and monitoring plans for AI systems • Instrument production monitoring and define measurable metrics • Mentor analysts on methodology and technical rigor