Description

You will lead end-to-end data science initiatives across multiple complex domains including fraud prevention, forecasting, inventory management, and price optimization.

Responsibilities

• Lead multiple data science projects from initial business requirement translation to deployment and long-term maintenance.
• Build and scale machine learning solutions for recommendation, assortment, and waste reduction.
• Develop Gen-AI workflows including RAG-based agents, embedding generation, and vector database retrieval.
• Manage the full model lifecycle, including prompt engineering, fine-tuning, monitoring, and governance.
• Deploy and maintain models within cloud environments to ensure operational efficiency.

Required Skills

• Extensive experience with Python, PySpark, and R.
• Deep expertise in Machine Learning: Classification, Regression, NLP, Forecasting, Unsupervised models, Optimization, Graph ML, and Causal Inference.
• Hands-on experience with Gen-AI: LLM gateways, vector databases, and iterative prompt engineering.
• Proficiency with Big Data platforms: Hadoop (Hive, MapReduce, HQL, Scala).
• Cloud experience using GCP or Azure, specifically Vertex AI and Kubeflow.
• Knowledge of GPU/CUDA for computational efficiency.
• Ph.D. with > 10 years of relevant experience or Master's with > 12 years of relevant experience in Computer Science, Statistics, Mathematics, or a related field.

Preferred Skills

• Experience with model selection and fine-tuning based on user feedback and accuracy metrics.

Key Skills

Education

Any Graduate