Senior Staff Engineer

Responsibilities:

• Personalized Experience: Own the end-to-end recommendation and personalization strategy across Homepage, PLP, PDP, and Cart, including problem definition, model development, online experimentation, production deployment, and business impact.
• 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: LLM-assisted retrieval, RAG for recommendations, user intent modelling, multi-modal embeddings, session summarization, and agentic personalization
• Mentorship and 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 and Innovation: Stay at the forefront of advancements in machine learning and recommendation systems, and translate cutting-edge research into impactful, scalable solutions for Nykaa.

Requirements:

• 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 (Retrieval: ANN search, Two-Tower, DSSM, PinnerFormer, and Ranking: DCN, DIN/DIEN, Transformers, Multi-Task Learning, and Calibrations).
• Strong programming expertise in Python, with hands-on experience in libraries/frameworks such as NumPy, Pandas, Scikit-learn, PySpark, TensorFlow/PyTorch, and Triton Inference Server.
• Familiarity with large-scale recommendation frameworks (e. g., FAISS, ScaNN, TorchRec).
• 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 consumer settings.