Data analyst who thinks like a builder.
I'm Abhishek, a Delhi-based data analyst. I turn messy data into decisions people can act on, and I build the tools that make those answers repeatable.
Building things
is essential.
I'm a data analyst who thinks like a builder. I'm the person who can't leave a weird number alone: when something looks off, I keep pulling the thread until I find the real reason behind it, not just a plausible one. Then I do the part most people skip. I automate the manual work around it, so the answer still holds up next month instead of being a one-time lucky catch.
Honestly, I care more about the decision at the end than the chart in the middle. Taking something scattered and messy and turning it into a clear "here's what's going on, and here's what to do" is the part I actually enjoy.
Outside of analytics, I founded and run Creative Moonlight Studio, a small creative and engineering studio that builds websites, web apps and AI-generated content for real clients. Running it taught me to move fast, own the outcome, and always design for whoever's on the other side of the screen.
When I'm not working, you'll find me behind a camera, deep in an anime series, or gaming.
"A jack of all trades is a master of none, but oftentimes better than a master of one."
Data-first
Python, Pandas, SQL. Turning messy data into decisions I can defend.
Builder mindset
React, Next.js, TypeScript, Supabase. I build the tools that make analyses repeatable.
Always building
Fast iterations, real users, honest metrics. No vaporware.
The stack behind the work.
Web Dev
Data Science / ML
Tools
$ npx create-next-app@latest && cd app$ bun add @supabase/supabase-js razorpay→ deploying in 3, 2, 1...
In [1]: import pandas as pd, sklearn as skIn [2]: df = pd.read_csv('churn.csv').dropna()Out : AUC = 0.87 · precision = 0.81
Where I've
done the work.
Founder / Data & Web Analyst
- Run the studio full-time, delivering data analysis, web apps and AI-generated content for clients end to end, from discovery through deployment and post-launch iteration.
- Build client websites and internal dashboards with Next.js, Supabase and Tailwind, turning scattered requirements into fast, reliable products.
- Design automated content pipelines using n8n and OpenAI APIs to produce marketing assets and social posts at scale with minimal manual work.
- Own client relationships, timelines and outcomes across multiple concurrent projects, balancing speed with work that holds up after go-live.
Sales Excellence Executive, Data & Analytics
- Designed and deployed 4 interactive Power BI dashboards for the MPS business unit, consolidating sales, operational KPIs and growth metrics into a single source of truth used by leadership in weekly strategy reviews.
- Built a standardized data-validation pipeline across 5+ internal sources, cutting reporting errors by ~30% and removing hours of manual cleaning each reporting cycle.
- Surfaced a 15% revenue decline in a product segment through trend and drill-down analysis; the finding triggered a targeted recovery initiative that reversed the trend within 2 weeks.
- Led end-to-end tracking for a 1,300+ PC distribution project: monitored shipment milestones, flagged 40+ operational exceptions, and coordinated cross-team fixes to reach 98% on-time delivery.
- Partnered with marketing and IT to launch the Iris Global and Iris Waves corporate sites, owning content structure, analytics-tag integration and go-live QA.
Business Analyst
- Built 4 Power BI dashboards on 2K+ customer records across subscription lifecycle, payment behaviour and demographics, enabling targeted campaigns that cut churn inquiries by 12%.
- Applied logistic regression and K-means clustering to flag high-risk churn segments; recommendations contributed to a 15% lift in targeted-discount conversions.
- Automated weekly data-cleaning workflows in Python (Pandas), reducing manual preprocessing from ~6 hours to under 1 hour per cycle and freeing 20+ analyst-hours per month.
- Delivered 40+ weekly and monthly performance reports, distilling complex behavioural patterns into 1-page executive summaries with clear action items.
Formal
training.
Master of Computer Applications (MCA)
Jain University, Bangalore
Bachelor of Computer Applications (BCA)
Maharaja Surajmal Institute, Delhi
Things I've built.
On GitHub.
ML in the wild.
Practice projects and notebooks as I sharpen my analytics and ML toolkit.
Customer Segmentation
K-Means on RFM features to group customers into clear behavioral archetypes.
Churn Prediction
Gradient-boosted model with SHAP explainability to surface why a customer is at risk, not just that they are.
Demand Forecasting
Comparing SARIMA and gradient-boosting approaches for weekly demand forecasting.
More coming soon
New analytics and ML experiments land here as I build them.
Try it yourself: a tiny analysis
200 synthetic study sessions across four topics. Filter, group, and pick a metric. Everything recomputes in your browser.
synthetic dataset, seed #1. no data leaves your browser.