Hemeshbabu

AI & Data Science graduate

Tirupati, Andhra Pradesh 85 · Excellent

About

Recent AI & Data Science graduate (2025) skilled in SQL, Python, Power BI, and data analytics. Experienced in building dashboards, performing data analysis, and developing machine learning solutions through academic projects.

Skills & Expertise (27)

Power BI Advanced

8.5/10

Years Exp

KPI Dashboard Development Advanced

8.5/10

Years Exp

Demand Forecasting Advanced

8.5/10

Years Exp

Python Advanced

8.5/10

Years Exp

Postgresql Advanced

8.0/10

Years Exp

Gradient Boosting Advanced

8.0/10

Years Exp

Random Forest Advanced

8.0/10

Years Exp

Logistic Regression Advanced

8.0/10

Years Exp

Predictive Modeling Advanced

8.0/10

Years Exp

EDA Advanced

8.0/10

Years Exp

Pivot Tables Advanced

8.0/10

Years Exp

Advanced Excel Advanced

8.0/10

Years Exp

scikit-learn Advanced

8.0/10

Years Exp

Pandas Advanced

8.0/10

Years Exp

SQL Advanced

8.0/10

Years Exp

NumPy Intermediate

7.5/10

Years Exp

Statistical Analysis Intermediate

7.5/10

Years Exp

Matplotlib Intermediate

7.0/10

Years Exp

Power Query Intermediate

7.0/10

Years Exp

Microsoft PowerPoint Intermediate

7.0/10

Years Exp

Data Storytelling Intermediate

7.0/10

Years Exp

Seaborn Intermediate

6.5/10

Years Exp

XLOOKUP Intermediate

6.0/10

Years Exp

VLOOKUP Intermediate

6.0/10

Years Exp

conditional logic Intermediate

6.0/10

Years Exp

DAX Intermediate

6.0/10

Years Exp

MySql Intermediate

6.0/10

Years Exp

Work Experience

Data Analyst

Bank Marketing Analysis

Present - Present

Preprocessed a 41,000-record imbalanced dataset; handled class imbalance using resampling and cost-sensitive techniques to ensure reliable model training. Benchmarked Logistic Regression, Random Forest, and Gradient Boosting classifiers; selected final model based on cross-validated ROC-AUC (~0.81) and cost-benefit analysis. Identified top predictors of customer response (call duration, prior campaign outcome, contact timing) via feature importance analysis; visualized findings for stakeholder review. Optimized decision threshold through cost-benefit analysis; documented methodology and evaluation steps for reproducibility.

BI Developer

Sales Performance Dashboard

Present - Present

Built an end-to-end BI pipeline ingesting Superstore transactional data via Python into PostgreSQL, then published a Power BI dashboard consolidating 10+ KPIs. Performed EDA to identify seasonal revenue patterns and category-level profitability drivers; surfaced actionable insights for inventory planning and promotional strategy. Automated ETL refresh logic to improve dashboard reliability and eliminate ad-hoc manual maintenance.

Data Scientist

Weekly Demand Forecasting

Present - Present

Engineered a continuous weekly demand time series for the top-10 products from 26 weeks of transactional data, including zero-fill logic to handle sparse demand periods. Compared naive baseline against a rolling 3-week average SQL forecast; the rolling model reduced Mean Absolute Error by 15–25% across products over the baseline.