Business Intelligence Machine Learning

Saachi Mogra

****.***********@*****.*** 628-***-**** https://tinyurl.com/smogra

EDUCATION

Stevens Institute of Technology, Hoboken, NJ Expected May 2025 Masters of Science in Business Intelligence & Analytics GPA: 3.83/4 Coursework: Data Management, Optimization & Process Analytics, Multivariate Data Analysis, Data Analytics & ML, Developing Business Applications using GenAI, Business Intelligence & Data Integration, Experimental Design, Marketing Analytics NMIMS Mukesh Patel School of Technology Management and Engineering Aug 2019 – Jun 2023 Bachelor of Technology in Electronics & Communication Engineering GPA: 3.46/4 SKILLS

• Programming: SQL, Python (Pandas, NumPy, Matplotlib, PySpark), C /C++, R

• Technologies: Data Analytics, Data Visualization, Database Administration, Data Modelling, Machine Learning

• Software & Tools: Microsoft Suite (Excel, PowerPoint, Word), Tableau, Erwin, Alteryx, Power BI, AWS, Bloomberg, PostgreSQL, MySQL, MongoDB, SAS, GitHub

• Concepts & Skills: Statistics, Algorithms, Regression Analysis, Data-Driven Decision Making, Hypothesis Testing WORK EXPERIENCE

Data Analyst- HealthPrevent360 Healthier Life Medicine PLLC, New York Aug 2024 – Dec 2024

• Automated workflows with Python and SQL querying, cutting data processing time by 30% and enabling real-time data integration from diverse sources via Alteryx into the HealthPrevent360 platform

• Applied R for statistical analysis and Python for machine learning to generate personalized preventive care recommendations for 100,000 users, increasing compliance by 15%

• Built predictive models using Python with 85% accuracy to identify at-risk populations, enabling proactive interventions that improved patient outcomes by 20%

Research and Development Intern Collateral Medical Private Limited Mumbai, Intern June 2022- July 2022

• Assisted in the implementation of Colposcope and Face Analyzer by researching technical specifications and business impact.

• Managed project coordination, including ordering equipment, developing testing procedures, and optimizing workflows.

• Delivered datasheets, budgets, and inventory tracking using Microsoft Excel to enhance efficiency and test accuracy. PROJECTS

Data Warehouse & Business Intelligence System Stevens Institute of Technology Jan 2024 – May 2024

• Aggregated data from Google Play Store API, web scraping, and third-party analytics to clean, normalize, and organize data into AWS data warehouse using Python (Pandas), increasing accuracy and efficiency by 25%

• Engineered schema design using PostgreSQL to efficiently store and retrieve large data volumes for the Google Play Store, improving performance by 30% through optimized indexing and partitioning

• Implemented Tableau for sales data visualization, predictive analytics, and forecasting in the Google Play Store, leading to a 15% revenue increase and a 32% boost in data communication efficacy E-Commerce Recommendations with FP-Growth Analysis Stevens Institute of Technology Jan 2024 –May 2024

• Enacted FP-Growth algorithm using MLxtend Library to analyze large e-commerce datasets, resulting in a 40% increase in the speed and scalability of frequent itemset detection for personalized product recommendations

• Leveraged Python to derive association rules from purchase patterns to strategically place products and promotions, leading to a 15% boost in sales and customer satisfaction on e-commerce platform

• Integrated insights on support, confidence, and lift using R and Python into a dynamic recommendation engine to tailor product suggestions based on individual customer preferences, enhancing the personalized shopping experience by 20% Supply Chain Optimization and Forecasting Stevens Institute of Technology Sept 2023 – Dec 2023

• Developed and implemented advanced data structures using Python and R to enhance demand forecasting accuracy, reducing inventory carrying costs by 15%

• Consolidated and cleaned extensive datasets using Alteryx, Python and R from various sources, ensuring data consistency and accuracy for supply chain decision-making, leading to a 20% improvement in forecast accuracy

• Analyzed, optimized, and visualized supply chain models using Python, R, and Power BI, identifying inefficiencies, and implementing improvements that reduced operational costs by 10%

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