Aditya Mohan Singh

Aspiring Data Analyst

Calicut 80 · Excellent

About

Aspiring Data Analyst (B.Tech, NIT Calicut) skilled in Python, SQL and Power BI. Built end-to-end projects in customer churn (28.6% churn traced to contract type, support escalations and state-level spikes) and retail supply chain (stockout, supplier-reliability and spoilage root-cause analysis). Strong in data cleaning, KPI design and turning analysis into clear business recommendations.

Skills & Expertise (19)

Python Advanced

8.0/10

Years Exp

Feature Engineering Advanced

8.0/10

Years Exp

SQL Advanced

8.0/10

Years Exp

Data Cleaning Advanced

8.0/10

Years Exp

MS Excel Advanced

8.0/10

Years Exp

Pandas Advanced

7.5/10

Years Exp

Power BI Advanced

7.5/10

Years Exp

correlation analysis Advanced

7.0/10

Years Exp

KPI Design Advanced

7.0/10

Years Exp

Jupyter Notebook Advanced

7.0/10

Years Exp

SQLite Advanced

7.0/10

Years Exp

NumPy Advanced

7.0/10

Years Exp

DAX measures Intermediate

6.5/10

Years Exp

Git Intermediate

6.5/10

Years Exp

GitHub Intermediate

6.5/10

Years Exp

Matplotlib Intermediate

6.5/10

Years Exp

Seaborn Intermediate

6.0/10

Years Exp

C++ EDA

Work Experience

Data Analyst

OTT Streaming Platform

Present - Present

Joined customer, subscription and support tables from a SQLite database; cleaned the data and engineered features: churn flag, tenure, complaint count and churn-risk bands. Calculated churn of 28.6%, retention of 71.4%, average tenure of 1,451 days and revenue lost to churn of about 18%. Found that monthly-plan customers churn at 55.6% vs 8.3% for annual plans (6.7x higher); customers with escalated complaints also churned more, and Karnataka had the highest churned-customer count with a September spike. Recommended annual-plan incentives, early outreach on escalated complaints, and a High/Medium/Low churn-risk list for the support team.

Data Analyst

Retail Inventory & Supply Chain

Present - Present

Analyzed a simulated 52-week inventory ledger (16 stores, 30 products, 10 suppliers); cleaned duplicates, inconsistent categories and missing values, and wrote 10 SQL queries for business questions. Found an overall fill rate of 98.2%, but Packaged Foods had the highest stockout rate (2.2%), pointing to reorder-point sizing and not to supply problems. Showed that supplier reliability (r = -0.44), not lead time, drives stockouts, and that spoilage cost is concentrated in Perishables; recommended smaller, more frequent perishable orders and reliability-based safety stock. Built a 4-week moving-average forecast with 7.2% MAPE on the highest-volume SKU; prepared a Power BI-ready dataset with DAX measures.