Ujjwal Sharma

About Me

I am a graduate student in the M.S. Business Analytics program at California State University, Sacramento, with a passion for data-driven decision-making, process improvement, and strategic communication. As the current President of the MSBA Association (MSBAA), I lead student engagement efforts and foster connections between academia and industry through events, mentorship, and collaboration. Professionally, I serve as a Graduate Intern at Sacramento State and have previously worked as a Program Analyst and Program Coordinator. My experience includes building interactive dashboards, conducting advanced data analysis, and developing machine learning models to uncover trends and support business outcomes. I’m proficient in tools and languages including SQL, Tableau, Power BI, Python, and Excel, and I thrive in environments where analytical thinking meets real-world application. I’m driven by the challenge of turning complex data into clear, actionable insights. With a background that blends technical skills and leadership, I aim to support organizations in making smarter, evidence-based decisions that create long-term value.

Professional Experience Timeline

CSU Sacramento - Academic Technology Intern (Data Analytics)

Aug 2025 – Present

Resolved over 4 ServiceNow tickets each month, boosting customer satisfaction and driving a 15% increase in positive feedback through timely and effective solutions.
Built 2+ BI dashboards in Tableau, enabling administrators to track academic technology adoption and metrics.
Remediated 3+ Excel, PowerPoint, and Word documents for accessibility using Microsoft tools, improving document usability for all users.

Institute for Social Research – Student Research Assistant

Mar 2025 – May 2025

Built and maintained Excel reporting suite from CATI exports, producing daily/weekly production dashboards.
Implemented duplicate detection and reconciliation checks (phone/ID/disposition matching, COUNTIFS/XLOOKUP audits) to remove repeat records and align Excel aggregates with CATI system totals, strengthening data integrity.

BACL Corp – Program Coordinator/Analyst

Feb 2023 – Jan 2024

Managed compliance of energy efficiency and safety programs, achieving a 100% audit success rate.
Conducted in-depth project data analysis to identify process inefficiencies, leading to a 15% improvement in time efficiency and supporting cross-departmental resource allocation and decision-making.
Created 3+ Tableau dashboards every week to show project metrics.
Orchestrated 5 daily projects, closely overseeing progress and facilitating engineer communication.

B.O.K Ranch – Project Assistant

Jan 2022 – May 2022

Facilitated multi-department coordination through Scrum methodology and Gantt charts. Designed a shared Excel template for customer data tracking and created tutorial content to support users.

Leadership Experience

Master of Science Business Analytics – President

Jun 2025 – Present

Led the MSBAA Events Committee by delegating tasks and mentoring students, successfully organizing and executing 5+ high-impact analytics-focused events.
Facilitated board meetings to ensure alignment across Events, Marketing, and Professional Development committees, improving overall committee effectiveness by 50 percent using Power Automate.

Technical Skills

Python

Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn, PyTorch, TensorFlow, Predictive Analytics, XGBoost

SQL

PostgreSQL, SQL, DBMS, Data Warehouse, Star Schema, Relational Databases, ETL pipelines

R Script

EDA, Regression, Hypothesis Testing, Statistical Modeling

Cloud

GCP (BigQuery, DataFlow, Pub/Sub, Cloud Data Fusion, Firebase), AWS (RedShift, S3, Lake Formation)

Other Tools & Technology

Microsoft Excel, Microsoft Office, GitHub Actions, CI/CD, Automation, Agile, ServiceNow, Power Automate, Tableau, PowerBI

Project Showcase

Spotify Song Popularity Prediction

Nov 2025 – Nov 2025

Built an ensemble regression pipeline to predict Spotify track popularity (0–100) from audio features (acousticness, danceability, energy, loudness, etc.) plus engineered signals like dance_energy and key one-hot. Used hold-out validation (random_state=1) and compared Bagging, Random Forest, and XGBoost with MSE/RMSE/MAE/R² and permutation importance. Chose XGBoost because it achieved the best validation (MSE ≈ 92.19, R² ≈ 0.8065) while training in minutes versus >40 minutes for the others. Its built-in regularization (learning rate, depth, subsampling, L1/L2) yielded better generalization and stable performance, making XGBoost the most practical, accuracy-per-minute winner for deployment.

House Sales Dashboard

Aug 2025

Designed and deployed an interactive Tableau dashboard leveraging filters, geographic mapping, and heatmaps to analyze 20K+ King County house sales records and uncover market trends.
Identified pricing patterns in 20K+ King County home sales, showing most fell between $200K–$600K with condition and view quality driving premiums.

Database Property Management System

April 2025 – May 2025

Implemented a robust OLTP database solution, improving transaction processing speeds by 40% and reducing data latency; ensured data integrity with comprehensive validation rules; and designed a star schema data warehouse to support advanced analytics and reporting.
Engineered SQL ETL pipelines and QA processes, ensuring 100% data accuracy across occupancy, maintenance, and revenue datasets, and delivered BI reports adopted by 5 departments.

Telecom Customer Churn Prediction

June 2025 – June 2025

Built an end-to-end ML pipeline in Python: handled skew (Yeo–Johnson), scaled & reduced dimensions (PCA), and compared 3 classifiers ( SVM, RandomForest, Logistic Regression). I also utilized joblib to export the models. Ran 5-fold CV: – Logistic Regression: 86.0% – Random Forest (100 trees): 91.5% – SVM (RBF kernel): 91.5%