Associate Data Scientist Job Details | Capgemini

Capgemini is seeking an enthusiastic Junior AI-Native Consultant to join its Energy & Utilities sector team. The role supports client projects by applying AI and machine learning techniques, developing intelligent solutions, working with cloud and data platforms, and contributing to client-facing workshops and deliverables.

What you'll do:

• Collaborate with senior consultants and client teams to identify business challenges and opportunities for AI-driven solutions in the Energy & Utilities sector
• Assist in the design, development, and deployment of AI/ML models, including Generative AI and Predictive AI solutions
• Support data collection, cleaning, and preprocessing activities for AI initiatives
• Work with leading cloud technologies (Azure, AWS, GCP) and their AI/ML services
• Utilize platforms such as Databricks, PySpark, and modern AI frameworks
• Develop agentic AI workflows and intelligent agents to automate tasks and improve operational efficiency
• Participate in client workshops and presentations, effectively articulating technical concepts to diverse stakeholders
• Contribute to project documentation, reports, and client deliverables
• Develop reusable assets, demos, and solution accelerators
• Stay current with emerging AI technologies and industry trends, particularly within the Energy & Utilities landscape
• Foster a collaborative environment, actively sharing knowledge and best practices within the team

What they're looking for:

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related quantitative field
• Minimum of 15 months of professional (non-internship) work experience in data science, AI, or machine learning roles
• Demonstrable background in the Energy & Utilities sector
• Foundational knowledge of Artificial Intelligence, Machine Learning, and Deep Learning concepts
• Proficiency in at least one AI-centric programming language (e.g., Python)
• Experience with:
• Generative AI concepts (GPT, Claude, LLMs)
• MLOps, model deployment, and monitoring
• LangChain and Retrieval-Augmented Generation (RAG) concepts
• REST APIs, JSON, Authentication, and Integration patterns
• Deployment tools (Azure DevOps, Docker, AWS ECS/EKS/Fargate) and CI/CD pipelines (AWS CloudFormation, CodeDeploy)
• Data engineering principles, including SQL and NoSQL databases (e.g., MySQL, MongoDB, Redis)