Lead Scientist, Data Science

XPO India Shared Services Position: Lead Data Scientist Team: XPO India Data Science team Location: Hyderabad/Pune Number of Position: 1 Relevant Experience: 7+ years   Position Overview We are seeking a highly skilled Lead Data Scientist with deep expertise in Generative AI, Retrieval-Augmented Generation (RAG), and productionizing advanced machine learning models . The ideal candidate will have strong experience in MLOps and ML Engineering , ensuring scalable, reliability, and secure deployment of AI solutions into production environments. This role requires both technical leadership and strategic vision to drive impactful AI initiatives across the organization.   Key Responsibilities

AI Solution Development

Design, build, and productionize advanced ML/AI models, including Generative AI and RAG-based architectures . Lead end-to-end model lifecycle: data preparation, feature engineering, training, evaluation, deployment, and monitoring.

MLOps & ML Engineering

Establish and optimize MLOps pipelines for continuous integration, deployment, and monitoring of ML models. Implement best practices for model governance, reproducibility, and scalability . Collaborate with engineering teams to ensure seamless integration of AI solutions into production systems.

Leadership & Strategy

Provide technical leadership and mentorship to data scientists and ML engineers. Partner with product and business stakeholders to translate complex AI concepts into actionable business value. Drive innovation by evaluating emerging AI technologies and frameworks.

Operational Excellence

Ensure AI models meet performance, reliability, and compliance standards. Develop monitoring frameworks for model drift, bias detection, and performance degradation . Champion responsible for AI practices and security-first approaches.

Required Skills & Experience

Technical Expertise

Proven experience in Generative AI (LLMs, diffusion models, transformers) and RAG pipelines . Strong background in MLOps (CI/CD for ML, model monitoring, orchestration tools like MLflow, Kubeflow, Airflow). Solid ML Engineering skills: Python, PyTorch/TensorFlow, distributed training, cloud platforms (AWS, Azure, GCP) . Experience with vector databases (e.g., Pinecone, Weaviate, FAISS ) and retrieval systems.

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