● Led the development of end to end, real time machine learning pipelines for fraud detection, ensuring high accuracy, low

latency, and scalability in high-volume transaction environments.

● Engineered sophisticated features from diverse streaming data sources including banking APIs, payment systems, user

behavior signals, and device intelligence to enhance model performance and significantly reduce false positives.

● Deployed and managed machine learning and deep learning models (XGBoost, LightGBM, Autoencoders, Isolation Forest,

Graph Neural Networks) using AWS SageMaker, Lambda, and Fargate, with containerization (Docker) and orchestration

(Kubernetes), reducing processing latency by 30% and scaling to handle 50% higher transaction loads.

● Consolidated and processed structured and cloud-based datasets from platforms such as SQL Server, PostgreSQL, BigQuery, and Snowflake to enable comprehensive fraud analysis and reporting.

● Implemented graph-based anomaly detection techniques to identify complex fraud patterns and uncover hidden relationships

within transaction networks, improving risk detection capabilities.

● Partnered with cross-functional teams including risk, compliance, and product to align fraud detection systems with

regulatory frameworks (GDPR, CCPA, SOX), contributing to a 10% improvement in audit outcomes.

● Developed interactive dashboards and visualization tools in Power BI and Tableau to provide real-time insights into fraud

trends, geospatial risk patterns, and operational performance for leadership teams.

● Established automated model monitoring and retraining workflows, incorporating CI/CD pipelines, A/B testing, and

explainability methods (SHAP, LIME) to maintain model performance, transparency, and regulatory compliance.

● Conducted in-depth analysis of historical e-commerce sales and user behavior data using Python and SQL, identifying

seasonality patterns, customer trends, and key performance drivers across digital channels.

● Built and evaluated predictive models (ARIMA, Prophet, LSTM, XGBoost) to forecast 12-month sales and predict customer

churn, contributing to a 5% decrease in churn and more reliable revenue projections.

● Designed and generated advanced features by integrating data from CRM systems, web analytics, advertising platforms, and

inventory sources to improve customer segmentation and personalization, resulting in higher campaign effectiveness.

● Developed automated ETL pipelines in collaboration with engineering and marketing teams, consolidating data from

Shopify, Magento, Salesforce, and Snowflake, reducing data processing time by 40% and accelerating reporting workflows.

● Leveraged PySpark, Pandas, and NumPy to process and analyze large-scale transactional datasets efficiently

within a distributed cloud-based environment.

● Deployed ML models through Flask, Fast API, and Docker, orchestrated with Kubernetes and AWS SageMaker, to

streamline deployment pipelines.

● Created real-time dashboards in Power BI and Google Data Studio to monitor KPIs, sales, marketing, and supply chain

performance, driving faster decision-making.

● Monitored model performance and system health using Prometheus, Grafana, and AWS CloudWatch, while maintaining

CI/CD pipelines in Jenkins and GitHub Actions for continuous improvements.

● Performed comprehensive analysis of high-volume healthcare datasets, including electronic health records (EHR), insurance

claims, and patient activity data, leveraging Python and SQL to uncover patterns in disease trajectories, readmission risks,

and treatment outcomes.

● Collaborated with clinicians and healthcare stakeholders to translate medical requirements into data science solutions,

ensuring models aligned with real-world clinical workflows and decision-making processes.

● Built data pipelines to ingest and harmonize multi-source healthcare data (EHR, imaging metadata, lab systems) using

Apache Spark and cloud-based architectures, improving data accessibility and consistency.

● Developed optimization models to improve hospital resource allocation (bed occupancy, staff scheduling), reducing patient

wait times and enhancing operational efficiency.

● Delivered interactive dashboards and reporting tools using Tableau and Power BI to track patient outcomes, hospital KPIs,

and model predictions, enabling actionable insights for both clinical and administrative teams.

● Created interactive dashboards in Power BI and Tableau to monitor key healthcare KPIs such as patient outcomes, hospital

utilization, and treatment efficiency, supporting data-driven decision-making by clinicians and administrators