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# Data Scientist

- **Company:** Capgemini
- **Location:** Houston, TX, United States
- **Salary:** $110,573.0 - $172,786.0
- **Contract:** Temporary contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Amazon S3, Business Analytics Applications, Data Analysis, R (Programming Language), Python (Programming Language), Machine Learning, Natural Language Processing, SQL Databases, Large Language Models, Prompt Engineering, Generative AI, Machine Learning Operations, Api Gateway, Software Coding, Programming Languages
- **Published:** May 14, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=7e734185da4a6b3f

## About the Role

Do you have experience in Software coding?

## Description

As a Data Scientist, you will work within the Analytics team to develop models using Natural Language Processing along with many other techniques.

 * Developing ML models that allow us to be prescriptive as opposed to reactive.
 * Staying informed of next generation data and analytical tools.
 * Converting ad hoc analyses into scalable solutions and tools that can be used on demand as self-serve.
 * Helping reveal actionable insights for individuals within various business functions
 * Cleaning, prepping and verifying the integrity of data for analyses. Demonstrable knowledge of common coding languages (Python, SQL, R) with ability to learn new skills and
 * skills in new environments.
 * Experience in Developing and Deploying GenAI Solutions: Building and fine-tuning generative AI/LLM solutions using AWS Bedrock and available foundation models like Claude, Llama, and Titan.
 * Building ML Workflows: Leveraging a wide range of AWS services (S3, Lambda, SageMaker, API Gateway, etc.) to build end-to-end machine learning workflows.
 * Implementing RAG Architectures: Designing and building Retrieval Augmented Generation (RAG) pipelines and developing custom embeddings and vector search solutions.
 * Prompt Engineering and Optimization: Creating effective prompt engineering strategies, evaluation frameworks, and model optimization techniques.
 * Collaboration: Working with cross-functional teams, including data engineers and software engineers, to integrate AI applications into existing systems and translate business problems into scalable AI solutions.
 * Monitoring and Governance: Ensuring data quality, security, and compliance, and setting up monitoring and governance for production-ready AI applications.


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