Lead Data Scientist

Capgemini•Brooklyn, NY

15d•Hybrid

About The Position

Capgemini is seeking a Lead Data Scientist to develop and deploy AI-driven solutions that address business problems and deliver customer and business outcomes. The role focuses on predictive modeling, personalization, recommender systems, optimization, generative AI, and responsible AI systems. The position offers multidisciplinary AI research and development using diverse data sources. The successful candidate will work as part of a team of researchers and practitioners, partnering with business stakeholders and using advanced AI/ML tools and platforms. Solutions may support customer engagement, personalization, recommendations, decision optimization, and customer insights. The role is based in Westlake, Texas; Burlington, Massachusetts; or Durham, North Carolina, and is described as hybrid with day-one onsite work.

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, predictive modeling, propensity models, recommender systems, deep learning, and graph-based learning techniques.
Experience with advanced NLP frameworks such as Transformers, generative AI solutions, and LLMs.
Experience formulating and solving optimization problems.
Knowledge of model evaluation, tuning, performance measurement, deployment, and solution scalability.
Strong verbal and written communication and presentation skills, including the ability to communicate effectively with business and technical teams.
Strong hands-on experience building predictive machine learning models, recommender systems, ranking models, and prescriptive optimization models.
Strong proficiency in Python, SQL, MLOps, and modern AI/ML development frameworks such as Scikit-learn, TensorFlow, PyTorch, Hugging Face, and AWS SageMaker.
Expertise across the AI/ML lifecycle, including experimentation, A/B testing, feature engineering, model development, deployment, monitoring, measurement, and governance.
Experience evaluating, deploying, and governing generative AI solutions, including RAG, knowledge retrieval, agentic AI workflows, LLM fine-tuning, and LLM evaluation frameworks.

Nice To Haves

Experience applying machine learning, deep learning, econometric, and foundation models to time-series forecasting.
Experience with knowledge graphs and graph-based learning approaches.
Experience with constraint or mathematical programming, optimization packages, and solvers such as IBM ILOG CPLEX, Gurobi, and Google OR-Tools.

Responsibilities

Develop and deploy AI-driven solutions for customer engagement, personalization, recommendations, decision optimization, and customer insights.
Build predictive machine learning models, recommender systems, ranking models, and prescriptive optimization models.
Work closely with business stakeholders to solve business problems using advanced AI/ML tools and platforms.
Conduct experimentation and A/B testing, engineer features, develop and deploy models, and monitor, measure, and govern solutions.
Evaluate, deploy, and govern generative AI solutions, including RAG, knowledge retrieval, agentic AI workflows, and LLM fine-tuning.

Benefits

Flexible work.
Healthcare, including dental and vision coverage, mental health, and well-being programs.
Financial well-being programs, including 401(k) and an Employee Share Ownership Plan.
Paid time off and paid holidays.
Paid parental leave.
Family-building benefits, including adoption assistance, surrogacy, and cryopreservation.
Subsidized backup child and elder care and tutoring.
Mentoring, coaching, and learning programs.
Employee Resource Groups.
Medical, dental, and vision coverage.
Retirement savings plans.
Life and disability insurance.
Employee assistance programs.