Experience:

- Masters degree in data science, computer science, applied mathematics, statistics, or a related quantitative field.
- 5-9 years of professional experience in advanced analytics and machine learning roles
- Experience in analyzing complex and ambiguous business problems and translating them into data science/AI

solutions.

- Strong expertise in Python, SQL, and experience with big data processing tools (Spark, Hadoop). Hands-on
- experience with deep learning frameworks (TensorFlow, PyTorch, Keras).
- Experience in machine learning, supervised and unsupervised learning: Natural Language Processing (NLP),
- classification algorithms, advanced data/text mining techniques, multi-modal model development, and deep
- neural network architecture
- Experience in statistical learning methodologies including but not limited to Predictive & Prescriptive Analytics,
- Web Analytics, Parametric and Non-parametric models, Regression, Time Series Forecasting, Market Basket
- Analysis, Dynamic/Causal Inference Modeling, Statistical Learning, Guided Decisions, Topic Modeling
- Familiarity with Autonomous AI systems design, implementation of human-in-the-loop frameworks, and
- expertise in AI system monitoring, observability and governance practices.




- Experience in end-to-end LLM implementation including custom embedding generation, vector database
- management, LLM gateway configuration, RAG-based agent development, strategic model selection, iterative
- prompt refinement, model fine-tuning for accuracy optimization, and implementation of robust monitoring and
- governance frameworks. Proficiency in modern orchestration frameworks such as LangChain or DSPy.
- Experience in MLOps best practices, including production-grade model deployment at scale, and
- designing/implementing enterprise-level AI/ML governance structures.
- Expert communication and collaboration skills with the ability to work effectively with internal teams in a crosscultural and cross-functional environment. Ability to communicate conclusions to both tech and non-tech
- audiences

Desired Skills:

- Retail industry experience and an understanding of retail/ecommerce data landscapes are robust pluses.
- Expertise in model interpretability techniques and ethical/responsible AI considerations.
- Familiarity with DevOps/DataOps practices for CI/CD of machine learning pipelines.
- Familiarity with Graph DB architecture.
- Proficiency in cloud platforms (AWS, GCP, Azure) and containerization (Docker, Kubernetes)

📌 Senior Data Scientist (Bengaluru)
🏢 RALPH LAUREN
📍 Bengaluru