Data Scientist

Capgemini

. Develop and apply machine learning models for forecasting and classification .

Posted 10/23/2026full-timeRemote • ColombiaMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and applying machine learning models for forecasting and classification, with hands-on experience in building GenAI products and implementing agentic solutions. Proficient in SQL, Python, and cloud platforms such as Azure and Databricks, with a strong focus on delivering data science and engineering services.

Highest-signal resume keywords

Machine Learning Model DevelopmentGenAI Product DevelopmentStrong SQL ProficiencyPython ProgrammingCloud Platform Experience

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningSQLPythonPySparkXGBoostLightGBMSHAPLLM APIsRAGVector Search

Soft Skills

Communication with Non-Technical Stakeholders

Tools & Technologies

Claude CodeCodexCursorAzureDatabricksGCP/VertexAWS/SageMaker

Industry Keywords

Data ScienceData EngineeringForecastingClassificationAgentic Solutions

Tech Stack

Tools & technologies

AWSAzureCloudGoogle Cloud PlatformPySparkPythonSQL

About the role

Key responsibilities & impact

Develop and apply machine learning models for forecasting and classification
Build GenAI products using RAG, vector search, and agents with tool use
Implement agentic solutions using Claude Code, Codex, or Cursor
Run LLM API batch or asynchronous jobs at scale for data labeling and metadata extraction
Use SHAP to explain and debug models for non-technical stakeholders
Work with cloud and data platforms including Azure, Databricks, GCP/Vertex, or AWS/SageMaker
Provide data science and engineering services to innovative companies across industries

Requirements

What you’ll need

Strong SQL: complex joins, CTEs, window functions, and query performance tuning
Strong Python
PySpark or similar
Real forecasting and classification work (demand forecasts, lead scoring, propensity) in production or serious projects; coursework does not count
XGBoost and/or LightGBM in production
SHAP to explain and debug models for non-technical stakeholders
Hands-on agentic coding with Claude Code, Codex, or Cursor
Built GenAI products including RAG, vector search, and agents with tool use
LLM APIs at scale: batch or async jobs with thousands of calls
Azure and/or Databricks preferred; strong GCP/Vertex or AWS/SageMaker experience is acceptable