Staff ML Scientist - Recommendation

Nykaa; Bengaluru; On-site; INR 1,800,000 - 2,500,000; Full time

Job summary

A leading e-commerce platform in Bengaluru is looking for an experienced Data Scientist to join their Recommendation & Personalization team. The role involves designing personalized user experiences, developing advanced recommendation systems, and mentoring junior team members. Candidates should have a strong background in data science and machine learning, with at least 7 years of industry experience. Knowledge of Python and related libraries is essential, along with a degree in a relevant field.

Qualifications

• 7+ years of industry experience in Data Science and machine learning, specifically in recommendation systems or personalization.
• Proven experience in designing and deploying recommendation systems.

Responsibilities

• Design and build a fully personalized landing experience.
• Develop intelligent systems to dynamically re-rank homepage assets.
• Build and deploy deep learning–based ranking models for recommendations.
• Explore and implement generative AI–driven recommendation approaches.
• Mentor junior team members and drive best practices.
• Stay updated on advancements in machine learning and recommendation systems.

Skills

Recommendation systems

Machine learning

Python

A/B testing

Data Science

Education

Bachelor’s or Master’s degree in Data Science or related field

Tools

NumPy

Pandas

Scikit-learn

PySpark

TensorFlow

Job description

About the Team: Nykaa’s Recommendation & Personalization Data Science team is responsible for building and optimizing personalized user experiences across the entire customer journey—from Homepage to Product Listing Pages (PLP), Product Display Pages (PDP), and Cart. The team drives personalization across all Nykaa businesses, including Beauty, Fashion, and Nykaa Man, leveraging data science and machine learning to enhance discovery, engagement, and conversion.

Responsibilities

• Personalized Experience: Design and build a fully personalized landing experience that maximizes user relevance, reduces decision friction, and drives higher revenue per user along with repeat engagement across Nykaa apps.
• Dynamic Homepage Optimization: Develop intelligent systems to dynamically re-rank homepage assets, minimizing user fatigue, improving discovery and exploration, and optimizing overall platform revenue while balancing monetization goals.
• Advanced Recommendation Systems: Build and deploy deep learning–based ranking models to deliver highly personalized recommendations leveraging user beauty profiles, skin analyzer insights, and in-app behavioral signals.
• Generative Recommendations: Explore and implement generative AI–driven recommendation approaches to enhance content personalization, discovery, and user engagement.
• Mentorship & Team Development: Mentor junior team members by owning KRAs, conducting code reviews, and driving best practices, while fostering a strong culture of learning, ownership, and innovation.
• Research & Innovation: Stay at the forefront of advancements in machine learning and recommendation systems, and translate cutting‑edge research into impactful, scalable solutions for Nykaa.

What are we looking for?

• Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, or a related field from a reputable institution.
• 7+ years of industry experience in Data Science and machine learning, specifically in recommendation systems or personalization.

Technical Skills

• Proven experience in designing and deploying recommendation systems (e.g., collaborative filtering, matrix factorization, deep learning–based recommenders).
• Strong programming expertise in Python, with hands‑on experience in libraries/frameworks such as NumPy, Pandas, Scikit-learn, PySpark, and TensorFlow or PyTorch.
• Familiarity with large-scale recommendation frameworks (e.g., TorchRec) is a plus.
• Strong foundation in Probability, Statistics, and core Machine Learning concepts.
• Deep understanding of A/B testing, incrementality measurement, and online experimentation, particularly in high-scale consumers.