Staff ML Scientist

Responsibilities:

• Personalised Experience: Own the end-to-end recommendation and personalisation strategy across Homepage, PLP, PDP and Cart, including problem definition, model development, online experimentation, production deployment and business impact.
• Dynamic Homepage Optimisation: Develop intelligent systems to dynamically re-rank homepage assets, minimising user fatigue, improving discovery and exploration, and optimising overall platform revenue while balancing monetisation goals.
• Advanced Recommendation Systems: Build and deploy deep learning-based ranking models to deliver highly personalised recommendations leveraging user beauty profiles, skin analyser insights, and in-app behavioural signals.
• Generative Recommendations: LLM-assisted retrieval, RAG for recommendations, user intent modelling, multi-modal embeddings, session summarisation, and agentic personalisation.
• 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 personalisation.
• Proven experience in designing and deploying recommendation systems (Retrieval: ANN Search, Two-Tower, DSSM, PinSformer; Ranking: DCN, DIN/DIEN, Transformers, Multi-Task Learning, 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 platforms.