Senior Data Scientist, IES CFX & Prime

Amazon Science; Bengaluru; On-site; INR 4,000,000 - 6,500,000; Full time

Job summary

Amazon Science in Bengaluru is seeking a senior data scientist to design and deploy scalable ML systems that enhance the shopping experience while protecting customers. You will own the end-to-end ML lifecycle from feature engineering to MLOps and collaborate with global teams to build production-grade models for millions of interactions.

The ideal candidate has 8+ years in data science, strong statistical modeling skills, and hands-on experience with production ML, GenAI, and fraud detection.

Qualifications

• 8+ years of experience in data querying languages (SQL) or statistical software.
• 8+ years of data scientist experience.
• Experience with statistical models e.g. multinomial logistic regression.

Responsibilities

• Work independently across the end-to-end ML lifecycle: feature engineering, modeling, and MLOps to productionize models.
• Develop and deploy ML systems to analyze large-scale customer data for fraud detection and marketplace security.
• Apply ML/statistical techniques to scale abuse-detection and identify fraudulent behavior.
• Collaborate with cross-functional teams to build scalable models spanning 10+ countries.
• Explore GenAI to build automated solutions for promotions and customer experience.
• Travel to Mexico and Brazil when required and align with CET hours for overlaps.

Skills

SQL

Python

Data Science

Tools

R

SAS

MATLAB

Job description

Description

Are you passionate about applying machine learning and advanced statistical techniques to protect one of the world's largest online marketplaces? Do you want to be at the forefront of developing innovative solutions that improve Amazon's customers experience? Do you thrive in a collaborative environment where diverse perspectives drive breakthrough solutions? If yes, we invite you to join the Amazon Customer Fulfillment & Prime analytics team. We're seeking an exceptional scientist who can revolutionize how we delight our customers. As a key member of our team, you'll develop and deploy machine learning systems that analyze millions of customer interactions daily, ensuring state of art experience for customers across 10 countries. Your work will directly impact the shopping experience for hundreds of millions of customers worldwide.

Key job responsibilities

• Ability to work independently across the end-to-end ML lifecycle: feature engineering, modeling, and MLOps to productionize models.
• Strong foundation in machine learning and statistical modeling.
• Experience deploying ML solutions into a production environment.
• Apply machine learning and statistical techniques to create scalable abuse-detection solutions that identify fraudulent customer behavior, rings of accounts, identity change, holistic customer risk, and marketplace manipulation schemes.
• Innovate with the latest GenAI technology to build highly automated solutions for efficient customer promotions.
• Collaborate with cross-functional teams to develop comprehensive ML/statistical models that scale to millions of customers across 10+ countries.
• Willing to travel to Mexico (MX) and Brazil (BR).
• Comfortable working CET hours to maintain overlap with Mexico and Brazil business teams.

About The Team

You'll be working closely with business partners, science and engineering teams to create end-to-end scalable machine learning solutions that address real-world problems. You will build scalable, efficient, and automated processes for large-scale data analyses, model development, model validation, and model implementation. You will also be providing clear and compelling reports for your solutions and contributing to the ongoing innovation and knowledge-sharing that are central to the team's success.

Basic Qualifications

• 8+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience
• 8+ years of data scientist experience
• Experience with statistical models e.g. multinomial logistic regression

Preferred Qualifications

• Experience as a leader and mentor on a data science team

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