Lead Scientist, Data Science

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.
  

• 
  

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  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.