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# Data Scientist

- **Company:** Capgemini
- **Location:** Atlanta, GA, United States
- **Experience:** Expert
- **Salary:** $70,000.0 - $140,000.0
- **Contract:** Permanent contract
- **Skills:** A/B Testing, Artificial Intelligence, Data Analysis, Big Data, BigQuery, Cloud Database, Cloud Storage, Data Governance, Extract Transform Load (ETL), Data Visualization, Database Queries, Data Flow Control, Python (Programming Language), Machine Learning, NumPy, Power BI, SQL Databases, Tableau (Software), Unstructured Data, Retrieval-Augmented Generation, Large Language Models, Snowflake, Prompt Engineering, Hallucination Detection, Model Validation, Pandas, Build Management, Scikit Learn, Looker Analytics, Amazon Redshift
- **Published:** October 9, 2026
- **Apply:** https://www.capgemini.com/jobs/571443-en_US_SAPBTP/x/

## About the Role

5+ years of experience in data analysis, business intelligence, or related analytics roles

Strong proficiency in SQL and data querying/transformation

Experience with Python (pandas, NumPy) or R for data analysis

Experience with BI/visualization tools (Tableau, Power BI, Looker, or similar)

Understanding of AI/ML concepts and exposure to evaluating model outputs or working alongside ML teams

Strong statistical analysis skills

Excellent communication skills - able to translate data insights for both technical and non-technical stakeholders

Experience working with large datasets in cloud data warehouses (BigQuery, Snowflake, Redshift)

Preferred Qualifications

Experience evaluating generative AI/LLM outputs (prompt quality, hallucination detection, response scoring)

Familiarity with GCP data tools (BigQuery, Looker, Vertex AI)

Experience with A/B testing and experimentation frameworks

Background in data governance or data quality frameworks

Exposure to Python-based ML libraries (scikit-learn) a plus

Good to have

GCP, Vertex AI, Data Modeling, ETL/ELT, Data Quality, Data Visualization, Prompt Engineering, RAG, Cloud Storage, Dataflow'

## Description

Analyze structured and unstructured data to identify trends, patterns, and actionable insights

Support evaluation of AI/ML and generative AI model outputs (accuracy, quality, bias, relevance)

Design and build dashboards and reports to track AI system performance and business KPIs

Partner with data scientists and AI engineers to define metrics for model evaluation and monitoring

Write and optimize SQL queries to extract and transform data from various sources

Perform exploratory data analysis (EDA) to support model development and feature identification

Assist in labeling, curating, and validating datasets used for training and fine-tuning AI models

Translate business requirements into analytical frameworks and present findings to stakeholders

Identify data quality issues and work with engineering teams to resolve them

Stay current on AI/ML trends relevant to data analysis and reporting