Senior Machine Learning Scientist – Ranking & Personalization

Position: Senior Machine Learning Scientist  – Ranking & Personalization
Location: Bengaluru

Machine Learning Scientist (MLS-2) – Ranking & Personalization

Role Description
As a Machine Learning Scientist (MLS-2) in Nykaa’s Search team, you will focus on the core of our discovery engine: relevance and ranking. Your mission is to ensure that every search query—from "red lipstick" to "summer skincare"—surfaces the most relevant, personalized, and high-converting products. You will build and scale Learning to Rank (LTR) models that balance user intent, historical behavior, and real-time signals to create a world-class shopping experience.

What are we looking for?

Education:

Bachelor’s or Master’s Degree in Computer Science, Machine Learning, Information Retrieval, or a related quantitative field.

Experience:

3+ years of hands-on industry experience in Machine Learning.
Proven experience in building and deploying ranking or recommendation systems or experience with Search Engines (e.g., Elasticsearch, Solr, Open Search) is highly preferred.
Technical

Skills:

Learning to Rank (LTR):  Strong understanding of ranking objectives and algorithms (e.g., Lambda

MART, Rank Net, or Gradient Boosted Trees).
Personalization:  Experience in developing user-state signals and embedding-based retrieval to personalize search results.
Signal Engineering:  Proficiency in feature engineering for high-cardinality data, including CTR, conversion rates, and real-time popularity signals.
Evaluation Metrics:  Expertise in Information Retrieval metrics such as NDCG, MRR, MAP, and Precision@K.
Tools:  Proficiency in Python, SQL, and Spark; experience with ML platforms like Sage Maker or Databricks.

Soft Skills:

Strong analytical mindset with the ability to diagnose "why" certain results are ranked the way they are.
Collaborative approach to work with Product and Engineering squads on A/B testing and production rollouts.

Responsibilities
Ranking Optimization:  Develop and refine Learning to Rank models to improve search relevance and conversion across Nykaa's platforms.
Personalization Engine:  Incorporate user-level preferences and historical interactions into the ranking stack to deliver a "segment-of-one" experience.
Query Understanding:  Enhance the search pipeline with models for query expansion, intent classification, and spell correction.
Multi-Objective Ranking:  Implement strategies to balance competing objectives like relevance, revenue, stock availability, and diversity in search results.
Experimental Rigor:  Design and analyze A/B tests for ranking changes, ensuring that every model deployment drives measurable improvements in North Star metrics (e.g., Search CVR, Add-to-Cart).
Signal Productionization:  Collaborate with data engineers to build robust pipelines for real-time and batch ranking signals.

What You’ll Learn & Grow Into
Scaling ranking systems that handle millions of queries and tens of thousands of products in real-time.
Deep mastery of modern Information Retrieval (IR) and Vector Search (Semantic Search) architectures.
Opportunities to contribute to the next-gen AI-driven discovery roadmap, including neural ranking and GenAI integration.