Worked on recall, ranking, and user-content understanding for large-scale advertising systems.

• Improved recall and ranking pipelines with gradient-boosted trees, deep neural networks, and sequential user representations.
• Fine-tuned BERT-based content encoders to strengthen personalization and user-content matching.
• Supported multi-objective modeling, A/B testing, and monitoring for stable iteration.

Built forecasting and anomaly-analysis pipelines for international distribution data.

• Integrated multi-source logistics data across sea, air, and road shipments.
• Applied ensemble and sequence models to cost, revenue, and anomaly prediction.
• Streamlined Python model-serving code and reduced system loading time by 40%.

Developed time-series and customer-segmentation models for advertising and retail campaign analysis.

• Analyzed revenue patterns and campaign performance with clustering and classification.
• Used language models to structure customer feedback and social content.

Modeled renewable-energy trends, project risk, and predictive-maintenance signals.

• Combined CatBoost, support vector regression, and Monte Carlo simulation for scenario analysis.
• Applied dimensionality reduction and anomaly detection to investment and maintenance questions.

Evaluated classical and ensemble models on large-scale security and public-data analysis tasks.

• Worked with Windows Defender telemetry and tested SVM, random forest, and XGBoost approaches.
• Translated model outputs into concise analytical deliverables for business and policy questions.