Data Scientist | Data Engineer | ML Engineer

Evaluated and validated outputs generated by large language models (LLMs), ensuring high-quality responses. Detected inconsistencies, biases, and factual errors in AI-generated content. Contributed to improving data quality for model training and evaluation pipelines. Worked under strict human review and quality assurance standards in AI evaluation workflows.

Designed and optimized automated data pipelines for geospatial and financial risk analysis, processing large-scale datasets using Python and GCP. Integrated satellite imagery and third-party mapping APIs into credit evaluation ML systems, enabling real-time geospatial risk scoring. Supported end-to-end deployment of machine learning models on Google Cloud Platform (GCP), improving credit decision accuracy by ~20%. Automated data validation, preprocessing, and quality control workflows using Python, reducing manual intervention time significantly. Collaborated with cross-functional remote teams to deliver scalable ETL pipelines aligned with business and compliance requirements.

Designed and implemented ETL and reporting automation pipelines using Python and SQL, eliminating repetitive manual workflows. Developed interactive dashboards and predictive reports in Power BI used daily by management teams for strategic decision-making. Reduced manual reporting time by ~30% through end-to-end process automation and scheduled data refresh pipelines. Analyzed large structured datasets to identify business trends, KPIs, and actionable insights for commercial and financial teams. Maintained and documented data workflows, ensuring data quality, consistency, and traceability across reporting systems.

Performed detailed financial analysis and automated reconciliation processes, improving accuracy and reducing processing errors. Supported data-driven financial decisions using accounting systems and structured reporting tools. Improved cash flow monitoring and regulatory compliance processes through systematic data review.