manan.jain
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Data analyst · Bengaluru, India

B.Tech CSE (AI) · Class of 2027

Manan Jain

n = 7,043 hover to brush · click to scatter
ROC-AUC 0.849 RECALL 81.3% REVENUE AT RISK FLAGGED $1.45M HIGH-RISK CHURN 2.6X BASE TOP DECILE CATCHES ~33% MANUAL DATA ENTRY -40% CAPTURE ACCURACY 85%+ WEEKLY REPORTING TIME -60% E-WASTE MARKET SIZED INR 20,000+ CR FLIPKART GRID 7.0 FINALIST CGPA 8.21 LATEST SGPA 9.06
A dot-matrix board spelling Manan Jain, drawn with 7,043 dots: one for each customer record in his churn project.

one dot for every customer in my churn dataset

I turn raw data into decisions people can act on.

Final-year Computer Science (AI) student at Manipal Institute of Technology, Bengaluru. Data analyst intern at Elevaste and Cisco, Google Data Analytics certified, and selected for the McKinsey Forward Program.

Get in touch See projects

about · 1 row

About.

I'm a final-year B.Tech student in Computer Science (Artificial Intelligence) at Manipal Institute of Technology, Bengaluru. My work sits where software engineering meets data analytics: Python and SQL to clean and shape the data, machine learning where it helps, and Power BI dashboards that put the answer in front of the people making the call.

At Elevaste I automated an image-based data-capture workflow and sized India's e-waste market. At Cisco I cleaned, queried and visualised business datasets in Excel, SQL, Tableau and Power BI. Alongside both, I've built churn, attrition and supply-chain analytics projects end to end.

I also compete in hackathons, most recently as a finalist at Flipkart Grid 7.0, and keep adding to the toolkit: I'm Google Data Analytics certified, a Google Cloud Digital Leader, and part of the 2026 McKinsey Forward Program.

Education

Manipal Institute of Technology, Bengaluru

B.Tech, Computer Science Engineering (Artificial Intelligence)

Jul 2023 – May 2027

CGPA 8.21 · Latest SGPA 9.06

Artificial Intelligence Machine Learning Database Management Systems Statistical Methods & Probability Data Structures & Algorithms Business Analytics

Jayshree Periwal High School, Jaipur

Class XII 83.2% · Class X 90.4%

Completed 2023

experience · 2 rows

Experience.

Two data analyst internships so far. The strip below maps my whole degree month by month, with internships and hackathons plotted on it.

Jul 2023 → May 2027

B.Tech CSE (AI), 47 months

Internship Hackathon Month done Now

Elevaste

Data Analyst Intern

Jan 2026 – Apr 2026
Mumbai

  • Automated an image-based data-capture workflow with Power BI reporting, cutting manual data entry by 40% and lifting accuracy to 85%+.
  • Sized India's ₹20,000+ crore e-waste market and designed database schemas for field-agent inputs, product catalogues and market-pricing data, turning business requirements into decision support for stakeholders.
-40%manual data entry
85%+capture accuracy
20,000+₹ crore market sized
Power BISchema designData modelingMarket sizing
Skills used here

Cisco

Data Analyst Intern

Jun 2025 – Aug 2025
Virtual

  • Cleaned, queried and analysed simulated business datasets in Excel and SQL through hands-on technical assignments, turning raw data into structured, decision-ready insights.
  • Built dashboards and visualisations in Tableau and Power BI, sharpening trend analysis and client-facing reporting.
ExcelSQLTableauPower BI
Skills used here

projects · 3 rows

Projects.

Three end-to-end analytics builds. Each one starts with raw tables and ends with a decision someone can make.

0.849ROC-AUC
81.3%Recall
$1.45MRevenue at risk / yr

Call the riskiest 10% of customers, and how many churners do you reach?

~10of every 100 churners if the 10% is picked at random

~33of every 100 churners using the model's top decile: a 3.3× lift

Churn rate against the base rate

Base rate1.0×
Riskiest segmentsup to 2.6×

Featured project · 7,043 customer records

Customer Churn & Retention Analytics

PythonSQLXGBoostPower BI
  • Engineered 27 behavioural features in SQL from 7,043 customer records and compared Logistic Regression, Random Forest and XGBoost, reaching 0.849 ROC-AUC and 81.3% recall.
  • Flagged $1.45M in annual revenue at risk and found segments churning at up to 2.6× the base rate.
  • Turned the scores into a top-decile targeting plan that reaches ~33% of churners by contacting just 10% of customers.

↑ the dots in the header are this dataset

View code on GitHub Skills used
1,470+Employee records
10+Dashboard KPIs
2Models compared
  1. 1,470+ raw HR recordssource data
  2. Python ETL pipelineclean · transform · load
  3. Normalised SQL schemaanalysis-ready tables
  4. Logistic Regression + Random Foresttrained under class imbalance
  5. Power BI dashboard10+ KPIs · attrition drivers

Workforce Analytics & Attrition Prediction

PythonSQLScikit-learnPower BI
  • Built a Python ETL pipeline and a normalised SQL schema that turn 1,470+ employee records into analysis-ready datasets.
  • Trained Logistic Regression and Random Forest models under class imbalance to score attrition risk.
  • Diagnosed the key attrition drivers and surfaced them in a 10+ KPI Power BI dashboard with recommendations for the business.
View code on GitHub Skills used
-60%Weekly reporting time
2Risk indicators
LIVEPower BI dashboard

Weekly reporting turnaround, old process = 100

Before100
After automation40

Risk indicators flagged across warehouses

Stock shortagesDelivery delays

Supply Chain Analytics Dashboard

PythonSQLPower BIPandas
  • Audited inventory and logistics records across multiple warehouses, pinpointing shortages and delivery delays as the key operational risk indicators.
  • Automated the data-preparation tooling behind a live Power BI dashboard, cutting weekly reporting turnaround by 60%.
View code on GitHub Skills used

skills · 35 rows

Skills.

Filter by where I've used each one. It works like a slicer in a Power BI report.

Used in

Showing all 35 skills

Languages & libraries

3
  • Python
  • SQL
  • Pandas

Analytics & BI

7
  • Power BI
  • Tableau
  • Excel
  • Dashboard Design
  • KPI Reporting
  • Data Visualization
  • Streamlit

Data management

8
  • MySQL
  • ETL
  • Data Modeling
  • Schema Design
  • Data Warehousing
  • Data Governance
  • Data Lakes
  • Data Quality

Machine learning & statistics

7
  • Scikit-learn
  • XGBoost
  • TensorFlow
  • PyTorch
  • Regression Analysis
  • Hypothesis Testing
  • Model Evaluation (F1, ROC-AUC)

Cloud & consulting

4
  • AWS Basics
  • Business Requirement Analysis
  • Stakeholder Management
  • Client-Ready Documentation

AI & LLMs

3
  • Prompt Engineering
  • Claude Engineering
  • Token Optimization

Tools

3
  • Git / GitHub
  • Jupyter
  • VS Code

awards · 6 rows

Wins & certificates.

Hackathons

Rank Event When
4th TCS BCIC Hackostav4th place Aug 2025
5th Honeywell Hackspace Hackathon5th place Oct 2025
Finalist Flipkart Grid 7.0National finalist Aug 2025

Certifications

McKinsey Forward Program

McKinsey & Company · 2026

Google Data Analytics Certificate

Google · 2026

Google Cloud Digital Leader

Google Cloud · 2025

contact · 3 rows

Let's talk data.

Working on a data problem, hiring for an analytics role, or want to compare notes on dashboards? Say hello.

LinkedIn in/manan-jain Connect GitHub @manan28025 See the code

based in Bengaluru, happy to hop on a call