Staff machine learning scientist – adtech
Location
bengaluru, karnataka
Job Type
Full-time
Category
Other-General
Posted
June 11, 2026
About the Team:
Nykaa’s Ad Tech Data Science team is at the forefront of building intelligent, ML-powered advertising products including Sponsored Products, Display Ads, and Creative Personalization. We aim to serve highly relevant, personalized ads to millions of users while maximizing advertiser success through scalable, automated systems. In this Staff MLS role, you will be a hands-on technical leader—driving the development of foundational ML models and systems that fuel our ad platform. You’ll work closely with product, engineering, and business teams to define and solve high-impact problems in advertising science.
What Are We Looking For?
Education: Bachelor’s, Master’s, or Ph. D. in Computer Science, Machine Learning, Statistics, or a related technical field from a reputable institution.
Experience:
7+ years of experience building production-grade ML systems, with 5+ years specifically in Ad Tech, performance marketing, or large-scale recommender systems.
Prove...
Nykaa’s Ad Tech Data Science team is at the forefront of building intelligent, ML-powered advertising products including Sponsored Products, Display Ads, and Creative Personalization. We aim to serve highly relevant, personalized ads to millions of users while maximizing advertiser success through scalable, automated systems. In this Staff MLS role, you will be a hands-on technical leader—driving the development of foundational ML models and systems that fuel our ad platform. You’ll work closely with product, engineering, and business teams to define and solve high-impact problems in advertising science.
What Are We Looking For?
Education: Bachelor’s, Master’s, or Ph. D. in Computer Science, Machine Learning, Statistics, or a related technical field from a reputable institution.
Experience:
7+ years of experience building production-grade ML systems, with 5+ years specifically in Ad Tech, performance marketing, or large-scale recommender systems.
Prove...