• Rebuilt evals framework from local to remote execution to minimize production drift, enabling iteration with confidence across ~1,500 SQL, Python and chart evals

• Developed RAG and embedding techniques for notebook search, significantly improving search accuracy to enable deflection of inbound user queries to existing notebooks

• Reduced agent caseload for card fraud by 40%, by improving the workflow from running a single XGBoost model in real-time to multiple models in real-time and in batch

• Migrated ACH and check fraud workfows from a rule-based to a model-based system, implementing the GCP components end-to-end – feature store, model training and inference

• Self-taught various topics of interest: functional programming (Haskell, Prolog, Idris), generative models

• Led task force to streamline production MySQL to S3 to Redshift pipeline, reducing end-to-end time from 16 to 8 hours with incremental loads and parallelized runs

• Migrated SQL runner framework from local to remote execution with Github Actions, AWS Codebuild and Terraform, in addition to reducing Docker image build times by 60%

• Developed SQL testing framework by standardizing interfaces and use of SQLAlchemy interactions; minimizing boilerplate with functional programming and code generation

• Coordinated incident response to system-wide errors, and recovered data loss with heuristics developed from analysis of historical patterns