• Developed and deployed machine learning models using TensorFlow and PyTorch for portfolio optimization, improving predictive accuracy of asset performance by 18% and enhancing investment decision strategies.

• Implemented MLOps pipelines using MLflow and Docker to streamline model versioning and deployment, reducing model release cycles by 35% and improving reproducibility across data science teams.

• Built NLP-based models leveraging transformers for financial text analysis, extracting insights from reports and improving sentiment classification accuracy by 22% for investment research workflows.

• Designed CI/CD pipelines for machine learning workflows, enabling automated testing and deployment, reducing manual intervention by 40% and ensuring consistent model performance across production environments.

• Conducted A/B testing on model-driven investment strategies, validating performance improvements and achieving a 15% uplift in strategy effectiveness compared to traditional baseline approaches.

• Engineered scalable ETL pipelines using PySpark and Apache Spark to process high-volume financial data, reducing data processing time by 40% and improving pipeline reliability for enterprise analytics systems.

• Built and deployed fraud detection models leveraging classification techniques, enhancing transaction monitoring systems and reducing fraudulent activity detection latency by 28% while maintaining high precision and recall balance.

• Designed data pipelines integrated with AWS services including S3 and Lambda, enabling efficient batch processing and improving data availability for downstream analytics and reporting teams by 35%.

• Implemented cloud-based data solutions using Azure Machine Learning and GCP BigQuery, optimizing query performance and

reducing data retrieval time by 25% across multiple financial reporting workflows.

• Developed interactive dashboards using Tableau and Power BI to visualize financial KPIs, improving stakeholder decision-making speed and reducing manual reporting cycles by 30% across client engagements.

• Automated data validation and quality checks within ETL workflows, reducing data inconsistencies by 27% and ensuring compliance with financial data governance standards across multiple enterprise systems.

• Cleaned and transformed raw transaction data using Python and SQL to resolve missing values and inconsistencies, improving dataset usability and reducing downstream reporting errors by 18% across internal systems.

• Analyzed customer transaction data using Pandas and NumPy to identify usage patterns, supporting segmentation initiatives that improved targeting strategies and increased campaign response rates by nearly 14% for financial products.

• Built classification models using Scikit-learn for churn prediction, applying feature engineering and cross-validation, improving baseline model performance by 16% and enabling better prioritization of high-risk customer segments.

• Applied anomaly detection methods on transaction records to flag unusual activity, assisting fraud teams in identifying potential risks earlier and reducing manual investigation workload by approximately 10% across selected datasets.

• Developed time series models to track financial trends, supporting monthly planning cycles and improving forecast accuracy by around 11% compared to previously used manual estimation approaches.