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Focusing
🎯
Focusing
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Achievement: StarstruckAchievement: Galaxy BrainAchievement: Pair ExtraordinaireAchievement: YOLOAchievement: Pull SharkAchievement: Quickdraw

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smanindokhan/README.md

πŸ‘‹ Hi, I'm Anindo

Data Analyst working across telco, food delivery, and ride-sharing.

I focus on:

  • Pricing & profitability
  • Growth & marketplace analytics
  • Data systems that support decisions

Portfolio: https://anindo-khan.vercel.app

πŸ”— Connect

LinkedIn

πŸš€ Core Focus Areas

Area Work
πŸ“Š Analytics Systems End-to-end tracking, pipelines, dashboards
πŸ’° Pricing & Revenue Margins, profitability, catalog economics
🚴 Marketplace Supply-demand balance, incentives, allocation
πŸ“ˆ User Behavior Funnels, cohorts, retention, attribution
βš™οΈ Automation Pipelines, scripts, workflow automation
πŸ” Data Quality Tracking accuracy, reliability, governance

🌍 Domain Experience

  • πŸ“‘ Telco / eSIM / Digital Products
    Pricing, affiliate tracking, product catalogs, analytics foundations

  • πŸ” Food Delivery Platforms
    Supply-demand optimization, rider incentives, operational analytics

  • πŸš— Ride-Sharing Platforms
    Growth strategy, dynamic pricing, churn, fraud signals

πŸ› οΈ Tech Stack

Data & Analytics

SQL Python Google Analytics Firebase

Data Engineering & Orchestration

Google Cloud dbt Airflow n8n Apps Script

BI & Visualization

Tableau Looker Metabase Redash

Spreadsheets & Ad-hoc Analysis

Microsoft Excel Google Sheets

πŸ“Š GitHub Stats

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  1. crypto-pipeline crypto-pipeline Public

    Production data pipeline: CoinGecko API β†’ PostgreSQL β†’ dbt β†’ Airflow orchestration, with 26 automated data quality tests

    Python 32

  2. ga4-analytics-pipeline ga4-analytics-pipeline Public

    End-to-end GA4 analytics stack: event taxonomy β†’ dbt models in BigQuery β†’ Looker Studio dashboard tracking funnel, retention, and revenue by channel

  3. pricing-analytics-case-study pricing-analytics-case-study Public

    Price elasticity analysis on Airbnb data β€” log-log regression finds inelastic demand (Ξ²=0.23), Monte Carlo simulation models revenue impact of pricing scenarios

    Jupyter Notebook

  4. customer-churn-analysis customer-churn-analysis Public

    Churn prediction with logistic regression (AUC 0.834) β€” identifies 2,198 high-priority customers with $196k monthly revenue at risk and ranks them by intervention priority

    Jupyter Notebook

  5. user-acquisition-funnel user-acquisition-funnel Public

    Full funnel analysis on Olist data β€” MQL to repeat purchase. CAC vs LTV by channel, channel efficiency ranking, and retention deep-dive to identify where to focus acquisition spend.

    Jupyter Notebook

  6. marketing-attribution marketing-attribution Public

    Compares first-touch, last-touch, linear, and time-decay attribution models on synthetic customer journey data. Shows how model choice directly drives budget allocation decisions.

    Jupyter Notebook