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EMAIL AVAILABLE PHONE NUMBER AVAILABLE Boston, MA LinkedInSUMMARY3+ years of experience in data analytics and supply chain methodologies to optimize operational efficiency and drive strategic initiatives. Proficient in utilizing programming languages such as Python, R, and SQL to extract, analyze, and interpret large datasets from systems like SQL Server, PostgreSQL, and SAP. Skilled in advanced analytics tools like Tableau, Power BI, and MS Excel to create impactful visualizations that inform data-driven decisions.SKILLSMethodologies: SDLC, Agile, Waterfall, JIT Project, Value Stream Mapping, Six Sigma, Kanban Languages & Libraries: Python, R, SQL, NumPy, Pandas, Matplotlib, SciPy, ggplot2 Databases: PostgreSQL, MongoDB, SQL Server, MySQLSoftware & Visualization: Tableau, MS Excel, Google Data Studio, Power BI, SAP Algorithms: KNN, Logistic Regression, Random Forest, K-means, Graph Algorithms Frameworks and Tools: Anaconda IDE, PowerPoint, Git, Spark, RStudio, Azure Supply Chain & Risk Management: Global Supply Chain Management, Strategic Sourcing, Vendor Selection, Conflict Resolution, CRM, Budgeting, DFMEA, PFMEA, Fishbone, FTA, Why-Why EDUCATIONMaster of Science in Computer Science Sep 2022 - Jan 2024 Boston University, Boston, MABachelor of Technology in Computer Science Aug 2018 - May 2022 Galgotias University; Greater Noida, IndiaEXPERIENCEBristol-Myers Squibb, MA, USA July 2023  Present Supply Chain Data Analyst Spearheaded a Just-In-Time (JIT) project, achieving a 25% reduction in inventory costs and a 20% improvement in lead times, enhancing overall supply chain responsiveness through meticulous demand forecasting and inventory optimization. Used Excel to set up pivot tables and create various reports using data from SQL queries. Conducted comprehensive risk analyses using DFMEA and PFMEA methodologies, reducing failure likelihood by 25% and ensuring compliance with stringent pharmaceutical quality standards. Utilized SAP and Power BI to develop real-time reporting dashboards, reducing operational costs by 10% and improving data transparency for strategic decision-making. Engineered demand and inventory planning strategies, leading to a 30% reduction in excess inventory and a 20% improvement in forecast accuracy, aligning production with market demand. Tata Capital, India May 2019  Feb 2022 Data Analyst Followed Agile testing methodology, participated in daily SCRUM meetings, and tested each Sprint deliverable. Gathered and cleansed diverse financial and economic data for analysis from 5 different sources using Python libraries. Prepared dashboards using calculated fields, parameters, calculations, groups, sets, and hierarchies in Tableau. Applied statistical techniques, machine learning algorithms, and visualizations to uncover patterns and predict investment performance. Coordinated with cross-functional teams to define data requirements and ensure seamless integration of new technologies, improving overall data infrastructure and processing efficiency. Developed and automated complex SQL queries using Views, Indexes, Triggers, Stored Procedures, and User Defined Functions, optimizing query runtime by 16%. PROJECTSDatabricks Formula 1 Racing Analysis Constructed an automated data pipeline in Azure to extract, transform, and load historical Formula 1 race data from the Ergast Developer API, achieving streamlined data management. Designed and executed SQL queries and scripts, integrating datasets for motorsport trend reports, and developed Databricks dashboards to visualize Formula 1 dynamics, aiding strategic decisions. The visual analysis also clearly depicted the historical trajectory of teams' performances, indicating a shift in competitive advantage from teams like Williams in the early years to Red Bull and Mercedes in more recent times.Content-Based Movie Recommendation Engine Developed a content-based movie recommendation engine in Python using machine learning techniques and libraries to enhance user engagement by recommending movies similar to the user's preferences. Utilized cosine similarity to analyze movie features and user behavior, enabling personalized recommendations, and deployed the recommendation system as a website on Heroku.