I am an accomplished Data Scientist and Full-Stack Engineer with more than five years of hands-on experience building machine learning solutions and production-grade web and mobile systems. I specialize in taking products from data extraction and transformation through model development, deployment, and operationalization.
I build end-to-end ML pipelines for fraud detection, salary prediction, public-health forecasting, sentiment analysis, topic modeling, and recommendation engines. My work emphasizes deployable, explainable models, robust validation, and measurable business impact.
My technical background includes Python, Go, Rust, SQL, CatBoost, XGBoost, SHAP, ONNX Runtime, Docker, Streamlit, Flutter, and Next.js. I combine data science expertise with full-stack engineering to deliver APIs, dashboards, web platforms, and mobile applications.
I have delivered fraud detection systems processing 11,400 financial transactions, salary prediction models across 4,653 job postings, and backend improvements that increased performance by 40% while reducing response times by 60%. I have worked across fintech, e-commerce, hospitality, public health, marketplaces, payments, and media-related products.
I bring cross-functional leadership, stakeholder communication, Agile project delivery, and remote-team coordination to my work. I prioritize security, regulatory compliance, scalability, model explainability, and product-focused execution.
I am motivated by continuous improvement and translating data insights into practical outcomes. I am prepared to contribute as a senior technical professional on teams building impactful data-driven products.
