Data Scientist

Job Description

Data Scientist II – Customer Technology

Company: Wayfair
Location: Bangalore, India
Work Mode: Hybrid
Experience: 3–5 years
Level: Data Scientist II
Domain: Customer Analytics / E-commerce / Data Science

Role Overview

The Customer Technology Data Science team focuses on understanding and optimizing customer behavior across Wayfair’s website and mobile applications. The role combines quantitative analysis, coding, advanced analytics and business problem-solving.

Key Responsibilities
Analyze large-scale datasets including clickstream, sales, product, logistics and customer data.
Identify trends, performance gaps, growth opportunities and customer insights.
Apply GenAI and LLMs to accelerate analysis and automate analytical workflows.
Develop KPI tracking and anomaly detection systems.
Perform:
Key-driver analysis
Forecasting
Cohort analysis
Anomaly detection
Design, execute and interpret A/B tests.
Collaborate with Product, Engineering and Business Strategy teams.
Build scalable dashboards and reports.
Develop optimized pipelines for multi-terabyte datasets.
Required Technical Skills

Programming & Data

Python / R / SAS / SPSS
SQL
Joins
Aggregations
Complex querying
Large-scale data analysis
Statistical modeling
Quantitative analysis

Analytics & Experimentation

A/B testing
Experimental design
Forecasting
Anomaly detection
Customer behavior analytics
KPI development

Visualization / Data Platforms

Looker — plus
Google Data Studio
Tableau
Power BI
BigQuery

Emerging AI

Generative AI
LLMs
Education
Bachelor’s degree in a quantitative discipline such as:
Computer Science
Computer Engineering
Analytics
Mathematics
Statistics
Information Systems
Economics
Master’s degree preferred.
Best-Fit Profile

This is a strong fit for a 3–5 year Data Scientist / Product Analyst / Customer Analytics professional with excellent Python + SQL + statistics + experimentation skills. Candidates who have worked with large datasets, KPI analytics, A/B testing and business-facing insights will be particularly relevant.

Bonus: E-commerce or retail analytics experience, plus exposure to Looker, GenAI and LLMs.