Data Scientist/Engineer
Data work for teams that need cleaner reporting, clearer logic, and decisions they can trust.
Background across finance, risk, and operations analytics. The work is strongest where source data is uneven, definitions drift, or reporting needs to hold up under real business use.
Systems thinker by habit, analyst by function.
Focus
Analytical SQL, reporting logic, reconciliation, and data quality assessment.
Comfortable across warehouse workflows, Spark-based preparation, and BI/reporting support.
Recent work spans remittance, banking, and insurance.
~$10M annual prevented payout losses
Processing reduced from days to hours
>90% lower refund-related contact rate
>50% improvement in claims-model MAE
Services
Focused, lightweight, practical.
Data Readiness
Profiling, validation, and cleanup work that makes source data usable for reporting and downstream analysis.
Typical stack: SQL, BigQuery, warehouse modeling
Analytical Workflows
Structured analytical workflows for recurring business questions across finance, operations, and product contexts.
Typical stack: SQL, Airflow, BI tooling, stakeholder analysis
Scale and Reconciliation
Support for larger, messier, or high-consequence datasets where reconciliation and preparation need more structure.
Typical stack: Spark, PySpark, SQL, batch pipelines