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Achievement: Pair ExtraordinaireAchievement: Pull Shark

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vaish1101/README.md

Vaishnavi Iyer — Data Analytics, Business Intelligence and Applied AI

✦ About Me

  I'm pursuing an M.Sc. in Data Analytics & Decision Science at RWTH Aachen University.

  I build analytics platforms and intelligent systems around real world problems, designed to provide a reliable analytical foundation for assessing risks, evaluating alternatives, and shaping well grounded business recommendations.

  I also bring 2+ years of industry experience in QA Automation, working with SQL systems, validation, and automation across production workflows.

  Looking for internship and working student opportunities in Analytics, Data Science & Applied AI.

✦ Tech Stack

Tech stack: Python, SQL, PostgreSQL, Power BI, Excel, Streamlit, pandas, NumPy, scikit-learn, Keras, Databricks, PySpark, Delta Lake, Azure, LangGraph, LLM APIs, RAG, FastAPI, Pydantic, pgvector, Git, GitHub Actions, Docker, Pytest, Selenium, Jira, Confluence


✦ Featured Work

EV Charging Expansion Intelligence. Regional analytics platform that integrates official German mobility and charging data into validated KPIs and dashboards to identify underserved regions and support infrastructure planning. Tech: Databricks, PySpark, Delta Lake, Power BI, GitHub Actions, Python.

Vintage Watch Parts Pricing & Turnover Intelligence. Commercial analytics system for a real vintage watch parts business, using evidence based pricing and turnover analysis to generate explainable market value recommendations and selling horizon estimates from historical and active marketplace listings. Tech: Python, DuckDB, PostgreSQL, ETL, eBay API, Streamlit.

Enterprise Analytics Copilot. Multi agent AI system that turns business questions into end to end analysis, orchestrating analytics and RAG workflows to investigate KPIs, trends, and performance and provide grounded, validated decision support. Tech: Python, LangGraph, LLM APIs, RAG, pgvector, Pydantic, FastAPI, Docker.

Churn Prediction & Retention Analytics. Predictive modeling and retention analytics system that identifies customers at churn risk, explains key drivers, and converts model outputs into prioritized retention decisions. Tech: Python, scikit-learn, Predictive Modeling, Model Evaluation, Retention Analytics, Power BI.


✦ Let's Connect

LinkedIn Email

Popular repositories Loading

  1. vaish1101 vaish1101 Public

    Python

  2. ev-charging-expansion-intelligence ev-charging-expansion-intelligence Public

    Regional EV charging provision screening across Germany using Databricks, PySpark, Power BI and official KBA, BNetzA and Destatis data.

    Python

  3. vintage-watch-parts-pricing-turnover-intelligence vintage-watch-parts-pricing-turnover-intelligence Public

    Commercial analytics and ETL pipeline for evidence based pricing, market intelligence and turnover estimation in sparse secondary markets.

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  4. churn-prediction-retention-analytics churn-prediction-retention-analytics Public

    Predictive modeling and retention analytics for churn risk, customer prioritization, and capacity aware retention decisions.

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  5. enterprise-analytics-copilot enterprise-analytics-copilot Public

    Governed analytics portfolio project with deterministic calculations, traceable evidence and a recorded frontend demo.

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  6. mnl-reformulation-marketing-analytics mnl-reformulation-marketing-analytics Public

    Multinomial Logit reformulations for discrete choice modeling and optimization in Marketing Analytics.

    Python