Gayathri Puram
Data Scientist @Walmart
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WORK HISTORY
Data Scientist @Walmart
WA, US
Spearheaded feature engineering and exploratory analysis on holiday and event-driven sales patterns, improving anomaly detection precision by ~30% across over 150+ category-subcategory-region combinations. Built scalable GCP BigQuery pipelines and orchestrated on Vertex AI and Jupyter notebooks that flagged pre, during, and post-holiday behaviors, covering 90+ holidays and event types, using impact window and custom calendar mapping logic. Applied Isolation Forest and statistical baselining to detect anomalies in daily sales data, identifying 10–15% more actionable deviations, later validated by business teams across 5+ regional zones. Designed and deployed 3+ interactive Quick Sight dashboards to track low-stock and out-of-stock trends, providing SKU-level visibility across 600+ clubs, reducing manual follow-ups by 40%. Partnered with analytics, replenishment, and business teams to embed anomaly insights into weekly reviews, cutting down investigation time by ~20 hours/month through early alerting and visualizations. Conducted a full-scale Redshift usage audit across 1.5 TB+ of data, identifying 100+ inactive tables; collaborated with 10+ POCs to deprecate or retain datasets, reducing query overhead by 18%.
EDUCATION
Arizona State University
Master of Science - MS
Amrita Vishwa Vidyapeetham, Coimbatore
Bachelor of Technology - BTech
ABOUT GAYATHRI PURAM
Data Scientist and AI/ML Engineer with 4+ years of experience designing and deploying scalable machine learning solutions across retail, finance, and government sectors. Proficient in Python, SQL, TensorFlow, PyTorch, and AWS services including SageMaker, Redshift, and Lambda, with a strong foundation in NLP, predictive modeling, and cloud-native AI architectures. Proven success in enhancing model accuracy, streamlining data workflows, and driving business intelligence through end-to-end ML pipelines and GenAI tools like Langchain and Hugging Face. Adept at Agile methodologies, cross-functional collaboration, and mentoring, with hands-on experience in deploying ML models via Docker, Kubernetes, and cloud platforms.
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