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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