{"data":{"jobs":{"edges":[{"node":{"frontmatter":{"title":"Data Science Intern","company":"AICTE","location":"Remote, India","range":"December 2024 - May 2025","url":"https://www.aicte-india.org/"},"html":"<ul>\n<li>Built an end-to-end ETL pipeline processing 5M+ retail transaction records using optimized Pandas workflows reducing data preprocessing time by 60%</li>\n<li>Engineered RFM behavioral features (recency, frequency, monetary) and applied K-Means clustering evaluated across 10+ initializations via Silhouette analysis, identifying 5 distinct customer segments</li>\n<li>Collaborated with a team of 10+ analysts and engineers to define segmentation criteria, validate cluster profiles, and align model outputs with targeted marketing objectives</li>\n<li>Deployed segmentation inference via Flask REST API achieving sub-100ms latency for real-time usage</li>\n<li>Drove 20% increase in conversion rates through segment specific behavioral profiling and personalized recommendations</li>\n</ul>"}},{"node":{"frontmatter":{"title":"Research Intern","company":"SPPU","location":"Pune, India","range":"January 2024 - September 2024","url":"https://aissmsioit.org/"},"html":"<ul>\n<li>Designed a MAML based meta-learning framework for retrieval-augmented code summarization across 108K Python code summary pairs, outperforming CodeBERT by 16% on BLEU-4</li>\n<li>Engineered a two-stage coarse-to-fine retrieval pipeline combining TF-IDF filtering with BERT semantic reranking improving METEOR by 34% and ROUGE-L to 43.06 over pretrained baselines</li>\n<li>Collaborated with a team of 5 researchers to benchmark 4 competitive baselines, evaluating generalization across procedural, OOP, and functional Python paradigms</li>\n</ul>"}}]}}}