Bikramdeep Singh

HIGHLIGHTS

• Researched and analyzed monthly sales trends by region, product category, and customer demographics to identify slow-moving inventory and strategic improvement opportunities.
• Flagged underperforming SKUs accounting for ~10% of total inventory; supported category managers in data-informed repricing, improving category-level sales by 3–5%.
• Identified seasonal demand patterns, market drivers, and pricing sensitivities, refining promotional strategies and timing for key product groups.
• Built structured performance reports to track product sell-through and inventory aging, enabling data-backed strategic planning and reducing stockouts in high-demand SKUs.
• Partnered with cross-functional teams (marketing, merchandising, strategy) to align insights with promotional campaigns, contributing to improved conversion rates in selected regions.

HIGHLIGHTS

• Cleaned and validated large volumes of U.S. healthcare claims data, improving accuracy and reliability for downstream reporting.
• Identified common data quality issues and supported enhancements that reduced recurring errors and manual corrections by approximately 11%.
• Ensured compliance with HIPAA and internal data handling standards, contributing to data integrity and audit-ready deliverables for analytical teams.

HIGHLIGHTS

• Developed and trained ML models for classification and clustering tasks using scikit-learn, TensorFlow, and PyTorch as part of an AI & Data Science postgraduate program.
• Applied NLP techniques and feature engineering to extract insights from unstructured text data, achieving high accuracy on benchmark datasets.
• Built end-to-end data pipelines from data acquisition through modeling and visualization, demonstrating full-cycle data science competency.
• Graduated with an 84.9 GPA and earned a place on the Dean's List for academic excellence.

HIGHLIGHTS

• Earned three Microsoft Azure certifications: Azure Data Fundamentals, Azure AI Fundamentals, and Azure Fundamentals.
• Designed and implemented data pipelines using Azure cloud services, integrating ETL workflows with Spark and Airflow for scalable data processing.
• Worked with big data technologies including Hadoop, Hive, and Kafka for real-time and batch data integration solutions.
• Containerized data applications with Docker and Kubernetes, enabling reproducible deployments and streamlined DevOps workflows.