In my role as an Operations and Data Student Specialist at the Basic Needs Center, I've been instrumental in driving impactful changes. I optimized data and metrics reporting processes with Tableau, resulting in a remarkable 30% improvement in transparent communication with stakeholders. Additionally, my efforts in comprehensive data collection contributed to a 15% enhancement in program impact through meticulous quantitative analysis. Furthermore, I spearheaded the overhaul of Pantry's organizational methods, leveraging data analysis techniques, and achieved a notable 20% increase in operational efficiency and regulatory compliance. It's been a rewarding journey, making data work for a meaningful cause! 📊✨

During the foundational phase, I spearheaded the tech stack migration for a US-based fashion retailer. By optimizing SnowSQL queries, I achieved a commendable 30% efficiency improvement and successfully migrated SAS functions to SnowSQL, ensuring a seamless transition to an optimized tech stack.In a pivotal phase, my focus shifted to designing an MVP framework for a US-based fashion retailer. I achieved 90% accuracy in predicting new product performance, analyzing nearly 4 million clickstream records with PySpark, leading to a 50% improvement in restock decision-making. Additionally, I engineered a forecast model with limited data, reducing overstocking costs by 60% through innovative feature engineering.Collaborating on another initiative, I supported a Canada-based retailer in daily business intelligence, optimizing data pipelines in Databricks with Python, and redesigning Tableau dashboards, resulting in a 50% improvement in visualization. This effort uncovered nearly 70% of data discrepancies through a proof-of-concept for Slack alerting and model monitoring using PySpark and Grafana.In this multifaceted project, I targeted demographic profiling for a major sportswear manufacturer, boosting revenue by 20% through user notifications and addressing product numbering challenges. Simultaneously, I elevated website personalization from 25% to 30%, using Adobe Analytics and historical clickstream data. Additionally, I contributed to a 15% improvement in customized product offerings for football enthusiasts by analyzing clickstream data with PySpark, considering gender and global market preferences.

During my tenure as a Full Stack Data Science Intern at TheMathCompany in Bengaluru, India, from January 2021 to May 2021, I undertook a pivotal capstone project focused on data-assisted cross-selling of credit cards to movie customers for a Middle East client. Spearheading the initiative, I implemented a streamlined data quality check, resulting in an impressive 80% reduction in manual task time. My role extended to executing comprehensive exploratory analysis and hypothesis testing using Python's Statsmodels library, elevating feature selection, and enhancing model performance by 60% through meticulous factor map analysis. Notably, I achieved a 70% AUC score and 77% recall in supervised machine learning, strategically reducing features from 48 to 17 using variance inflation factor analysis. This experience significantly contributed to my expertise in data science and machine learning applications.