Lead Data Scientist

Capgemini•Dallas, TX

2d•$110,841 - $145,000•Hybrid

About The Position

Capgemini is seeking a Lead Data Scientist to develop and deploy AI-driven solutions across customer engagement, personalization, recommendation, decision optimization, and customer insights. The role offers multidisciplinary AI research and development with access to diverse data sources. The successful candidate will work with business stakeholders as part of a team of researchers and practitioners, using advanced AI/ML tools and platforms to address business problems and deliver meaningful customer and business outcomes. The position is listed for Westlake, Texas; Burlington, Massachusetts; or Durham, North Carolina, and is described as day-one onsite with a hybrid arrangement.

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, GenAI solutions, and LLMs.
Experience formulating and solving optimization problems.
Knowledge of model evaluation, tuning, performance measurement, deployment, and solution scalability.
Strong verbal, written, 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 and platforms 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 GenAI 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 working with knowledge graphs and graph-based learning approaches.
Experience with constraint or mathematical programming, optimization packages, and solvers such as IBM ILOG CPLEX, Gurobi, or Google OR-Tools.

Responsibilities

Develop and deploy AI-driven solutions for customer engagement, personalization, recommendation, decision optimization, and customer insights.
Work closely with business stakeholders and a team of researchers and practitioners to solve business problems using advanced AI/ML tools and platforms.
Build predictive machine learning models, recommender systems, ranking models, and prescriptive optimization models.
Work across the AI/ML lifecycle, including experimentation, A/B testing, feature engineering, model development, deployment, monitoring, measurement, and governance.
Evaluate, deploy, and govern GenAI solutions, including RAG, knowledge retrieval, agentic AI workflows, LLM fine-tuning, and LLM evaluation frameworks.

Benefits

Flexible work.
Healthcare, including dental, vision, 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.
Disaster relief.
Medical, dental, and vision coverage.
Retirement savings plans.
Life and disability insurance.
Employee assistance programs.