Senior Data Scientist | 7+ YOE | Fraud Analytics, Machine Learning, AWS

I’m a results-driven Data Scientist with 7+ years of experience building machine learning solutions, fraud detection systems, and scalable data pipelines in enterprise environments. I specialize in turning complex data into measurable business impact, combining statistical expertise, production-grade engineering, and clear stakeholder communication. I thrive in collaborative environments where insights directly inform business decisions.

Core Expertise & Tools:

• Machine Learning & AI: Supervised/unsupervised learning, fraud detection, deep learning, Generative AI, LLMs
• Data Engineering & Big Data: Apache Spark, Hadoop, Databricks, NiFi, Airflow, CI/CD
• Programming: Python, Oracle SQL, MySQL, Hive
• ML Frameworks: scikit-learn, XGBoost, H2O.ai, PyTorch, Spark MLlib
• Cloud & Monitoring: AWS, Google Cloud, OpenSearch/Kibana, Splunk, Grafana
• Visualization: Tableau, Power BI

Key Achievements at Verizon (via Randstad Digital):

• Built ML models that cut unauthorized transactions by 45% and fraudulent registrations by 65%.
• Designed end-to-end data pipelines for automated model deployment and scalable fraud monitoring.
• Delivered $1M+ monthly savings through A/B testing, threshold optimization, and cost-benefit analysis.
• Reduced false positives by 47%, improving detection quality and customer experience.
• Led analytics during AWS cloud migration, ensuring data accuracy and observability.
• Partnered with stakeholders to translate data insights into actionable decisions, driving operational and strategic impact.

I hold an Master's Degree in Computer Science, an AWS Cloud certification, and have published research papers on optimization algorithms for churn prediction and feature selection. I’m seeking senior data science or ML roles where I can build production-scale AI systems, mentor teams, and create measurable business value.