Current Role and Primary Focus
Cassie is currently a senior engineering leader focused on applied machine learning and data infrastructure at a major fintech company. She oversees teams building real-time risk and personalization systems that process billions of transactions annually. Her work emphasizes scalable pipelines, model monitoring, and responsible AI practices in production environments.
Before her current position, Cassie held senior roles at several high-growth startups and large technology firms. She has led cross-functional teams that shipped customer-facing products and internal platforms used by thousands of engineers. Her background spans quantitative research, software engineering, and product strategy, with a focus on turning complex data into reliable business outcomes.
Key Projects and Technical Contributions
In her recent work, Cassie has led initiatives around fraud detection, credit underwriting, and customer analytics using modern ML stacks. She has championed the adoption of feature stores, online inference services, and automated model governance frameworks. These systems aim to improve latency, reduce false positives, and maintain compliance with evolving regulatory expectations.
Cassie has also contributed to open-source tooling and internal platforms that standardize experimentation and deployment across data science and engineering teams. She frequently speaks on topics such as ML observability, data quality, and the operational challenges of running models at scale. Her technical writing and talks often reference real case studies from production systems in finance and e-commerce.
Industry Context and Public Profile
Cassie is active in the broader fintech and AI community, participating in conferences, panels, and mentorship programs. She focuses on practical lessons for teams adopting ML in regulated industries, including data privacy, auditability, and incident response. Her public appearances emphasize clear communication between technical and business stakeholders.
Industry coverage and interviews highlight her perspective on how companies can balance innovation with risk management. She has shared insights on hiring, team structure, and the importance of robust data foundations for reliable AI systems. Her current work continues to influence how organizations approach machine learning operations in finance and adjacent sectors.