Data Analyst
Posted Updated
Summary
Data Analyst at insurtech Exzeo (Noida) who collects, cleans, and analyzes US property insurance data — scraping public sources with Python, processing it with Pandas/NumPy and SQL, and building reports and visualizations. P&C insurance domain experience is mandatory; GIS skills are a plus.
Position Overview:
The candidate will be responsible for collecting, transforming, and analyzing data from various sources, including web-based platforms and spatial databases. The ideal candidate will possess strong skills in acquiring data from publicly available sources (via web scraping, bulk data download) and data manipulation, preferably with a good understanding of US Property & Casualty Insurance.
Domain Expertise Requirement:
• Experience in the Property & Casualty Insurance sector is mandatory. The candidate should be well-versed with property insurance concepts, workflows, and business requirements.
• In-depth knowledge of property attributes (e.g., construction type, occupancy, age, location risk factors, valuation parameters, etc.) and experience working with data related to these attributes.
• Familiarity with catastrophe (Cat) events affecting property insurance, including wind, flood, hurricane, tornado, hail, and other natural perils. The candidate should be capable of integrating cat event data with property exposure datasets and analyzing impacts for underwriting and claims analytics.
Requirements
Key Responsibilities:
1. Data Collection & Web Scraping
• Develop and maintain web scraping scripts using Python (e.g., Beautiful Soup, Scrapy, Selenium) to extract data from publicly available sources and online directories.
• Schedule and automate scraping tasks while ensuring compliance with website terms of use and data privacy standards.
• Parse and clean extracted data, handling duplicates, encoding issues, and inconsistent formats.
2. Data Processing & Analysis.
• Perform data cleaning, transformation, and normalization across large datasets.
• Generate summaries, statistics, and insights using Python (Pandas, NumPy) and Excel.
• Merge and relate data from multiple sources, including tabular and geospatial inputs.
3. Database Operations & SQL
• Store and manage cleaned data in relational databases such as PostgreSQL.
• Write and optimize SQL queries to extract, join, CTE and filter datasets for analytical reporting.
4. Reporting & Visualization
• Present the interactive data stats/details tools like Excel, or Python libraries (Matplot, Plotly etc.).
• Export processed data and analysis results to CSV, Excel, or database tables for downstream use.
• Prepare well-documented reports for internal and client-facing use.
5. Good to Have: GIS Knowledge
• Familiarity with QGIS, shapefiles, and spatial joins.
• Familiarity with Python GIS libraries like Geopandas, GDAL/OGR, Shapely etc.
• Ability to visualize geospatial datasets and perform basic GIS tasks like buffering, measuring distance, and exporting styled maps.
Tools & Technologies Used:
• Programming: Python (Pandas, NumPy, Beautiful Soup, Scrapy, Selenium)
• Database: PostgreSQL, MongoDB, Json Objects
• GIS Tools (Good to Have): QGIS, MapInfo, PostGIS
Benefits
•Be part of a growing technology-first organization transforming the insurance industry.
•Enjoy a 5-day work week, a modern work environment, and cross-functional collaboration.
•Competitive salary and performance-driven career growth.
•Opportunity to work on challenging problems with cutting-edge tools and technologies