AI & Data Analyst Internship – Furgality Fintech (Pune, India)

Opportunity Overview

Role Scope & Data Pipeline Architecture

The AI & Data Analyst Intern works across the entire data lifecycle to transform raw transaction datasets into automated business insights and machine learning models:

[Web/API/PDF Scraping] ➔ [Data Cleaning & SQL/NoSQL Pipelines] ➔ [EDA & Analytics Dashboards] ➔ [ML/NLP Predictive Models]

Core Responsibilities

• Data Ingestion & Extraction: Extract structured and unstructured data using web scrapers (BeautifulSoup, Selenium, Scrapy), REST APIs, and document parsers.

• Pipeline Engineering & Data Wrangling: Clean, validate, and structure messy transaction datasets using Pandas/NumPy; build and maintain SQL (PostgreSQL) and NoSQL (MongoDB, Firebase, Supabase) schemas.

• Exploratory Analytics & Visualisation: Perform EDA to identify financial trends and anomalies; construct reports and dashboards using Matplotlib, Seaborn, Plotly, or BI tools (Power BI, Tableau).

• AI/ML & NLP Modelling: Build, evaluate, and deploy machine learning models (Scikit-learn) for transaction categorisation, credit/financial scoring, customer clustering, and recommendation engines.

• Workflow Automation: Write Python automation scripts to eliminate manual reporting workflows and support product/business intelligence decision-making.

Candidate Qualifications & Technical Matrix

Minimum Requirements

• Core Language Depth: Strong proficiency in Python for data processing, scripting, and automation.

• Database & SQL: Solid understanding of SQL querying, relational database design, and data normalization.

• Data Stack: Hands-on experience with Pandas, NumPy, Matplotlib, Seaborn, and Excel/Google Sheets.

• Web Scraping Tools: Experience extracting data using BeautifulSoup, Selenium, Scrapy, or API integration.

• Machine Learning Fundamentals: Practical knowledge of core ML algorithms, classification, regression, and Scikit-learn.

• Mindset: Strong logical reasoning, comfort handling uncurated real-world financial data, and adaptability to a fast-paced startup environment.

Technical Stack Matrix

Selection Workflow & Assessment Pipeline

[Resume & Portfolio Screening] ➔ [Data Scraping & Python Coding Challenge] ➔ [Data & ML Technical Interview] ➔ [Startup Execution & Culture Round]

1. Portfolio & Resume Screening: Review of Python data projects, web scraping repositories, SQL proficiency, and location feasibility for Baner, Pune.

2. Technical Data Assessment: Hands-on practical test involving Python data cleaning, web scraping from mock APIs/sites, writing complex SQL queries, and basic EDA.

3. Technical Interview Loop: Deep dive into data manipulation logic, database schema design, machine learning model evaluation metrics, and NLP/categorization algorithms.

4. Final Hiring Manager Round: Scenario-based evaluation focusing on problem-solving speed, analytical rigor, and alignment with Furgality Fintech’s fast-paced environment.