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Provides technical leadership for applied AI and data science initiatives from experimentation through production. Builds machine learning and generative AI solutions for anomaly detection, malicious behavior detection, fraud analysis, and security use cases. Designs LLM, retrieval, embedding, and agentic workflows; establishes evaluation and monitoring practices; partners with engineering and MLOps on reliable deployment; advises stakeholders; mentors technical contributors; and promotes secure, scalable, and responsible AI practices.

Lead applied AI projects from prototype to production, building and validating ML and GenAI models (LLMs, embeddings, RAG, agentic workflows) for malicious-behavior, anomaly, and fraud detection. Advise product and engineering teams, design experiments and evaluation pipelines, translate research into scalable APIs/workflows, and ensure data governance, privacy, and ethical practices.

Analyze product usage, feature adoption, telemetry, and financial data to generate actionable insights. Own data modeling, dashboards, KPI development, and end-to-end analytics delivery while partnering with stakeholders on product initiatives. Mentor associate-level analysts and maintain strong execution metrics for analytics work. The role requires advanced SQL and Python, Databricks and PySpark expertise, visualization experience, and SaaS product analytics experience.

Design, build, and maintain scalable data pipelines, optimize Databricks/Spark and Delta Lake architectures, deliver trusted datasets for analytics and ML, implement observability and data quality, and drive platform architecture and team mentorship.