• Design, develop, and implement advanced deep learning models, including transformers, CNNs, and LSTMs, to address complex fraud and risk challenges. • Build and optimize models using a variety of input data types, including tabular data, natural language, point clouds, and images. • Take ownership of assigned tasks, executing technical and functional activities to support project goals with minimal supervision. • Participate in all stages of the machine learning lifecycle: data exploration, feature engineering, model training, evaluation, and deployment. • Collaborate effectively across teams, sharing knowledge and learning from diverse perspectives to drive results. • Make routine technical decisions and contribute to functional objectives through productive and proactive engagement. • Stay current with advancements in AI and machine learning, applying innovative approaches to real-world problems. • Communicate results and insights clearly to both technical and non-technical audiences.

• Design, develop, and implement advanced deep learning models, including transformers, CNNs/RNNs, and graph learning algorithms, to address complex fraud and risk challenges. • Build and optimize models using a variety of input data types, including tabular data, natural language, point clouds, and images. • Lead the end-to-end machine learning lifecycle: data exploration, feature engineering, model training, evaluation, deployment, and monitoring in production environments. • Take ownership of project outcomes, data quality, and delivery timelines; proactively escalate issues and work collaboratively to resolve challenges. • Mentor and share knowledge with peers and junior data scientists, fostering a culture of experimentation, rapid iteration, and continuous learning. • Collaborate cross-functionally with Product, Engineering, and Risk teams to define data requirements and drive insights that guide strategic decisions. • Conduct in-depth research to explore new data sources and develop novel algorithms that advance the state of the art in fraud detection. • Present findings and recommendations to technical and executive stakeholders with clarity and influence. • Stay current with advancements in AI and machine learning, applying innovative approaches to real-world problems. • Model Socure’s embedded leadership competencies: continuous learning, effective communication, accountability, team development, decision making, and managing change.

• Develop and maintain SentiLink’s fraud detection models through the full model development lifespan: from data acquisition decisions through featurization, focusing labeling resources, model training, experimentation, productionalization, and monitoring. • Build foundational modeling to drive SentiLink’s expanding suite of Fraud and Financial Risk products. • Research new types of fraud and develop new SentiLink products around identity verification. • Achieve success by researching / developing through iteration, integration of new data sources and inventive feature engineering. • Write production-ready code that can be relied on for real-time decision making by our partners. • Design, perform, and present analyses that will inform data acquisition, product development, risk operations priorities, marketing, and sales efforts. • Work with engineering, risk operations, and data acquisitions to access necessary data, maintain data quality, and support data access

• Serve as lead when working collaboratively with product leads, managers, and owners to draw insight and intelligence from large administrative claims datasets, electronic medical records, and various healthcare registry datasets; • Identify and develop solutions that can be implemented to key strategic business problems using high-level modeling and statistical analyses techniques; • Utilize SQL to perform advanced-level data extraction, transformation and data management; fully document and manage library of source code and algorithms for future use; • Identify, develop, and test hypotheses in support of research and product offerings, and communicate findings in a clear, precise, and actionable manner to our clients; • Respond to and lead highly complex operational data requests and create ad-hoc queries to support research projects; • Serve as lead when working closely with Inovalon’s Enterprise Data Platform teams to identify, understand, and resolve data issues and improve the efficiency, productivity, and scalability of the company’s data processes; • Troubleshoot complex production-related issues and coordinate successful resolution; • Assist with the evaluation of data analytic vendors and tools; • Maintain compliance with Inovalon’s policies, procedures and mission statement; • Adhere to all confidentiality and HIPAA requirements as outlined within Inovalon’s Operating Policies and Procedures in all ways and at all times with respect to any aspect of the data handled or services rendered in the undertaking of the position; • Fulfill those responsibilities and/or duties that may be reasonably provided by Inovalon for the purpose of achieving operational and financial success of the Company; • Uphold responsibilities relative to the separation of duties for applicable processes and procedures within your job function;