Shivam Rajeshkumar Modi
Boston, MA 857-***-**** ******.***********.****@*****.*** www.linkedin.com/in/shivamrajeshkumarmodi/
EDUCATION
Master of Science in Analytics September 2023 - March 2025
Northeastern University, Boston, USA
MBA in Marketing + B.Tech in Electronics and Telecommunication Engineering July 2016 - April 2021
Narsee Monjee Institute of Management Studies, Mumbai, India
TECHNICAL SKILLS
Analytics & BI Tools: Power BI, Tableau, Looker Studio, Qlik Sense, Alteryx, AWS QuickSight, Excel (Power Query, PivotTables, VBA)
Programming & Databases: Python (pandas, seaborn), SQL (MySQL, Snowflake, Oracle), R, MongoDB, dbt, SSIS
Cloud & Big Data: AWS (S3, RDS, Redshift, Lambda), Azure (Data Factory), Snowflake
Business Systems: Salesforce, SAP, Workday, PowerApps, SharePoint, MS Visio, CRM Tools
Project Management: JIRA, Confluence, Agile/Scrum, UAT, BRD/FSD Documentation, Stakeholder Communication
Core Competencies: A/B Testing, Forecasting, KPI Reporting, Root Cause Analysis, Data Storytelling, Business Process Optimization
WORK EXPERIENCE
Business Analyst, FundzBazar, Ahmedabad, India Aug 2022 - Aug 2023
Conducted user behavior analysis using surveys and behavioral data, leading to a 23% increase in user engagement by optimizing customer journeys.
Implemented and analyzed A/B testing using Google Analytics, Neo4j, and Python, reducing customer drop-off rates by 14% and improving project performance.
Developed and maintained Power BI dashboards and analysis reports, providing actionable insights that improved data-driven decision-making for executives.
Collaborated with 3 cross-functional teams to refine project requirements and streamline internal data workflows, increasing team efficiency.
Devised data pipelines and reporting processes using SQL and Power BI, ensuring real-time tracking of KPIs.
Associate Consultant – Business Analyst, PwC Acceleration Center, Karnataka, India July 2021 - July 2022
Led the automation of invoice processing by integrating Salesforce – Oracle CPQ services using JSON, reducing processing time by 30% and improving operational efficiency.
Managed sprint meetings, project backlogs, and requirement-gathering sessions using JIRA and MS Visio, ensuring 21% fewer post-deployment issues.
Delivered interactive financial reports using Tableau, streamlining data analysis for senior stakeholders.
Conducted detailed requirement-gathering sessions with stakeholders and project managers to define technical specifications, leading to the successful deployment of user-centric solutions.
Advised teams on improving data workflows, resulting in increased operational efficiency and data accuracy.
ACADEMIC PROJECTS
Operational Efficiency Enhancement Through Data-Driven Decision Making
Developed comprehensive dashboard analyzing 200+ guest survey responses, revealing critical 48% expectation-reality gap using Power BI, DAX, and statistical analysis; created performance quadrant framework identifying the 5 most impactful operational improvement opportunities
Engineered custom data model with gap analysis and continuation rate metrics that quantified feature fulfillment rates; designed 5 interactive visualizations revealing that 83% of guests experienced fewer impressive features than expected despite 11 marketed amenities.
Translated analytical insights into strategic recommendations prioritizing evidence-based marketing realignment and operational improvements; demonstrated potential to increase guest satisfaction by 15-20% through targeted enhancements to underperforming but high-importance features.
Renewable Energy Market Analysis Project
Analyzed renewable energy adoption trends by applying K-means clustering and advanced time series modeling (ARIMA, Random Forest, SVR) to identify country-specific adoption patterns and forecast UK capacity growth to 2030.
Engineered comprehensive data pipeline using Python (Pandas, NumPy, Scikit-learn, Statsmodels) to clean UNSD Energy Statistics data, calculate capacity factors, and extract key metrics for production, consumption, and market segmentation.
Developed interactive visualization dashboard with Matplotlib and Seaborn to present energy flow analysis, stakeholder opportunities, and blockchain integration potential for renewable energy markets.