Lead Data Scientist

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

Lead Data Scientist role at Capgemini, focused on delivering AI and predictive solutions across customer engagement, decision optimization, and responsible GenAI use cases.

Responsibilities

• Develop and deploy AI-driven solutions across customer engagement, personalization, recommendation, decision optimization, and customer insights.
• Partner with business stakeholders to apply advanced AI/ML tools and platforms to solve business problems.
• Build end-to-end solution capabilities spanning model development, deployment, evaluation, and scalability across AI use cases.
• Design and operationalize responsible AI systems as part of AI solution delivery.
• Drive AI/ML initiatives across multiple customer engagement and decision optimization scenarios.

Requirements

• Master’s or Ph.D. degree in Computer Science, Data Science, Operations Research, Statistics, Applied Mathematics, or a related discipline.
• 10+ years of prior work experience in Artificial Intelligence, Machine Learning, Data Science, Advanced Analytics, or related technical fields.
• Experience with unsupervised and supervised machine learning, including predictive modeling and propensity models.
• Hands-on experience with recommender systems, deep learning, and graph-based learning techniques.
• Experience working with advanced NLP frameworks (Transformers, etc.), GenAI solutions, and LLMs.
• Experience formulating and solving optimization problems.
• Knowledge of model evaluation, tuning, performance measurement, deployment practices, and solution scalability.
• Strong verbal and written communication skills, including presenting findings to both business and technical teams.
• Strong hands-on capability building predictive machine learning models, recommender systems, ranking models, and prescriptive optimization models.
• Proficiency in Python, SQL, MLOps, and modern AI/ML frameworks including scikit-learn, TensorFlow, PyTorch, and Hugging Face.
• Experience using AWS SageMaker and similar platforms for AI/ML delivery.
• Expertise across the AI/ML lifecycle: experimentation, A/B testing, feature engineering, model development, deployment, monitoring, measurement, and governance.
• Experience evaluating, deploying, and governing GenAI solutions, including RAG, knowledge retrieval, agentic AI workflows, LLM fine-tuning, and LLM evaluation frameworks.

Technologies

• Python, SQL, MLOps
• Scikit-learn, TensorFlow, PyTorch, Hugging Face, AWS SageMaker
• Transformers, LLMs, RAG
• GenAI, A/B testing
• Graph-based learning techniques, recommender systems
• Deep learning, propensity models, feature engineering

Nice to Have

• Experience applying ML, DL, Econometric and Foundation Models for time series forecasting.
• Experience working with knowledge graphs and graph-based learning approaches.
• Experience with constraint and mathematical programming, optimization packages, and solvers (IBM ILOG CPLEX, Gurobi, Google OR-Tools, etc.).

Benefits

• Flexible work
• Healthcare including dental, vision, mental health, and well-being programs
• Financial well-being programs such as 401(k) and Employee Share Ownership Plan
• Paid time off and paid holidays
• Paid parental leave
• Family building benefits including adoption assistance, surrogacy, and cryopreservation
• Social well-being benefits such as subsidized back-up child/elder care and tutoring
• Mentoring, coaching and learning programs
• Employee Resource Groups
• Disaster Relief
• Paid time off based on employee grade (A-F), defined by policy: Vacation: 12-25 days depending on grade; Company paid holidays; Personal Days; Sick Leave
• Medical, dental, and vision coverage (or provincial healthcare coordination in Canada)
• Retirement savings plans (e.g., 401(k) in the U.S., RRSP in Canada)
• Life and disability insurance
• Employee assistance programs

Salary Transparency

• Base salary range for the tagged location: $110,841 to $145,000 per year.
• This role may be eligible for additional compensation, including variable compensation, bonus, or commission.
• Full time regular employees are eligible for paid time off, medical/dental/vision insurance, 401(k), and other benefits to eligible employees.

Location: Brooklyn, NY (hybrid)