Lead Data Scientist: GenAI & AI Solutions (Hybrid)

Capgemini; New York (NY); Hybrid; USD 111,000 - 145,000; Full time

Benefits offered by this job

Healthcare coverage

401(k) plan

Paid time off

Parental leave

Professional development

Job summary

Capgemini is seeking a Lead Data Scientist to develop and deploy AI-driven solutions across customer engagement, personalization, and decision optimization. You will collaborate with business stakeholders to apply advanced AI/ML tools and deliver end-to-end capabilities across model development, deployment, evaluation, and governance.

The role emphasizes responsible AI design, GenAI/LLMs, and scalable solution delivery, with a strong emphasis on cutting-edge NLP frameworks and MLOps practices.

Qualifications

• Master’s or Ph.D. in Computer Science, Data Science, Operations Research, Statistics, Applied Mathematics, or related discipline.
• 10+ years of experience in AI/ML/Data Science, analytics, or related fields.
• Experience with unsupervised and supervised machine learning, predictive modeling, and propensity models.

Responsibilities

• Develop and deploy AI-driven solutions across customer engagement, personalization, recommendation, and decision optimization.
• Partner with business stakeholders to apply AI/ML tools and platforms to business problems.
• Build end-to-end solution capabilities spanning model development, deployment, evaluation, and scalability.

Skills

Python

SQL

MLOps

scikit-learn

TensorFlow

PyTorch

Hugging Face

AWS SageMaker

GenAI

NLP frameworks

GPT/LLMs

Data visualization

Education

Master’s or Ph.D. in CS/Data Science/OR/Statistics/Applied Mathematics

Tools

Python

SQL

MLOps

Scikit-learn

TensorFlow

PyTorch

Hugging Face

AWS SageMaker

Job description

Capgemini is seeking a Lead Data Scientist to develop and deploy AI-driven solutions across customer engagement, personalization, and decision optimization. You will collaborate with business stakeholders to apply advanced AI/ML tools and deliver end-to-end capabilities across model development, deployment, evaluation, and governance.

The role emphasizes responsible AI design, GenAI/LLMs, and scalable solution delivery, with a strong emphasis on cutting-edge NLP frameworks and MLOps practices.