Anza Abbasi

Transformed and modeled critical financial datasets to produce accurate P&L, income, and accounting reports, automating complex integration and reducing manual effort by 70%. Built end-to-end Azure and Microsoft Fabric ETL/ELT pipelines with robust data quality, lineage, and governance to support month-end closings, compliance audits, and forecasting.

Implemented GPT-based solutions including chatbots, intelligent document summarization, automated report generation, and content recommendation systems. Applied NLP techniques (classification, NER, semantic search, topic modeling, sentiment analysis) and integrated GPT/LLM workflows using RAG with embeddings and vector databases.

Designed and orchestrated data pipelines using Apache NiFi and Apache Airflow for structured/unstructured ingestion, validation, anomaly detection, and ML workflow scheduling. Built fintech data integration and reconciliation systems, including automated reconciliation that reduced manual effort by 60% and improved fraud detection and regulatory reporting data integrity.

Developed machine learning models for regression, classification, clustering, and time series forecasting to derive actionable business insights. Forecasted demand patterns that reduced inventory costs by 25% and improved product availability, and used customer behavior and sales analysis to increase campaign effectiveness by 15%.

Developed and applied machine learning models and performed Python-based data manipulation. Integrated data from APIs, carried out feature engineering, and implemented multiple ML algorithms to solve business problems.

Grade: 3.7/4

MS in Data Science from Bahria University, completed in 2018–2020. Earned a CGPA of 3.7/4.

Grade: 3.6/4

BS in Computer Science from COMSATS University Islamabad, completed in 2015–2018. Earned a CGPA of 3.6/4.