YT

Yuvaraja T

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I turn raw data into clear answers. An ECE engineer by training, I'm building my career in data analytics with SQL, Python, Excel, and Power BI.

About

I turn raw data into clear answers. An ECE engineer by training, I’m building my career in data analytics with SQL, Python, Excel, and Power BI.
My path isn’t the usual one. I graduated in Electronics and Communication Engineering in 2013, then spent years preparing for competitive banking exams. That gave me things no course can: discipline, consistency, and the ability to solve hard quantitative problems under pressure.
In 2025, while preparing for the EXIM Bank exam, I picked up SQL and Python for the first time, and something clicked. Finding patterns in data and turning them into decisions was work I wanted to do every day, so I made the switch to data analytics.
Today, I’m working as a Data Analyst Intern at Alvionx, applying these skills to real business data.
What I bring
• Excel: formulas, pivot tables, data cleaning, reporting
• SQL: querying, joins, aggregations, pulling insights from databases
• Python (Pandas): data cleaning, wrangling, exploratory analysis
• Power BI: interactive dashboards and visual reports
• Engineering mindset: structured problem-solving and attention to detail
What I’m looking for
A full-time data analyst role where I can turn data into insights that help teams make better decisions. Banking, finance, and business analytics interest me most, but I’m open to any domain where data drives decisions.
Let’s connect. I’m always happy to learn from people working in data.

  • Joined November 2026

More experts in Namakkal, India

Projects · 1

Analyzed 10,000 customer records to find which segments churn most and why. Validated the data, compared churn rates across geography, gender, and activity status, and built a reusable high-risk customer flag for retention targeting.

Key findings:
• Overall churn was 20.4%, but Germany reached 32.4%, about double France and Spain
• Inactive women in Germany were the highest-risk segment at 44.6% churn
• Inactive members churned at up to 2x the rate of active members in the same segment
• Churned customers in Germany held the highest average balance (~120K), which raises the revenue impact

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