Balaji Reddy

Hey, I'm

Balaji Reddy

I turn complex data into clear business decisions.

What Drives Me

Solving problems and learning something new every day.

When I'm offline

Gaming, listening to music, and questioning myself.

What sets me apart

I adapt to whatever happens, without any resistance.

Skills

Data Analysis & Visualization
Product Analysis & Strategy
Financial Modeling
Stakeholder Management
Process Mapping
ETL
Data Modeling
SWOT Analysis

How I work?

Business first. Data always.

01

Frame

Turn an open-ended business need into measurable questions.

02

Model

Clean, validate, and structure raw data for trustworthy analysis.

03

Analyze

Use data visualization and business logic to uncover the drivers.

04

Communicate

Translate data insights into clear decisions that align with business objectives.

The Grind So Far

PayFlow

UPI payments analytics

A simulated UPI payments app benchmarked against PhonePe, Google Pay and Paytm, built end to end with Python, Snowflake, dbt, SQL and Power BI.

Q1

Which users are most likely to churn, and what do they share?

Inactive users churn most, sharing long inactivity and failed sessions, not low value.

Q2

Does the new payment flow actually lift success, or does it just feel better?

Yes, completion rate and speed both improved together, confirming a real lift.

Q3

How does PayFlow stack up against the real players?

PayFlow's regional mix matches real PhonePe data within 0.08 percentage points.

Affirm BNPL Analysis

Fintech

A financial deep-dive on Affirm using its FY2022 to FY2024 SEC filings plus a Kaggle transaction dataset, delivered as a 10-tab Excel model and a 6-page Power BI dashboard.

Q1

Is the lending business becoming more profitable?

Operating loss narrowed while revenue grew steadily.

Q2

Are credit losses improving over time?

Credit Loss Rate declined, indicating better risk control.

Q3

Will free cash flow turn positive?

Forecast shows positive FCF by FY2029 under the base case.

Online Retail Analytics

Ecommerce

A stakeholder-driven analysis of an online retail dataset, framed around the questions a CEO and a CMO would actually ask.

Q1

Which products, countries, and time periods drive the highest revenue growth?

Electronics generated 42% of revenue, the United States led all markets, and Q4 delivered the highest sales due to holiday demand.

Q2

Which customer segments generate the highest lifetime value, and where should retention efforts focus?

VIP customers contributed the highest lifetime revenue, while At-Risk customers represented $3.97M in recoverable revenue, making them the top retention priority.

Q3

Which products sell together, and what should we bundle?

Customers purchasing laptops frequently include a mouse in the same order. Offering a "Laptop Essentials Bundle" with a 10% discount could increase Average Order Value while reducing accessory purchase friction.

Certifications

01

Google Analytics Certification

02

Data Visualisation: Empowering Business with Effective Insights Tata Job Simulation, Forage

Getting bored? Try penalty shootout

Pick a spot and try to beat the keeper. Corners are riskier, but harder to save.

Goals
0
Shots
0
Streak
0
Best
0

Mix up your corners. The keeper remembers nothing, but a real one would.