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# Data Scientist - Data Platforms - I&D

- **Company:** Capgemini
- **Location:** St. Louis, MO, United States
- **Experience:** Experienced
- **Salary:** $76,918.0 - $120,203.0
- **Contract:** Temporary contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Cloud Computing, Computer Programming, Databases, Information Engineering, Data Infrastructure, Decision Support Systems, Python (Programming Language), Machine Learning, NumPy, Tensorflow, Azure Machine Learning, SQL Databases, Google Cloud, Cloud Platform System, Feature Engineering, Data Ingestion, Pytorch, Large Language Models, Model Validation, Multi-Cloud, Database Performance, Generative AI, HybridCloud, Pandas, Scikit Learn, Kubernetes, Information Technology, Data Management, Machine Learning Operations, Oracle Cloud Infrastructure, Data Pipelines
- **Published:** August 11, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=61fbca34991c8dbc

## About the Role

* Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Engineering, or related field.
 * 12+ years of experience in data science, analytics, or machine learning.
 * 3+ years in a leadership or senior technical role.
 * Strong programming skills in Python (Pandas, NumPy, Scikit-Learn, TensorFlow, PyTorch).
 * Experience with SQL and large-scale databases.
 * Expertise in statistical modeling and machine learning algorithms.
 * Experience deploying ML models in cloud environments (AWS, GCP, OCI).

Preferred Qualifications:

 * Experience with LLMs, RAG frameworks, or Generative AI applications.
 * Knowledge of ML Ops tools (MLflow, Kubeflow, SageMaker, Vertex AI).
 * Experience in database performance analytics or infrastructure optimization.
 * Familiarity with compliance frameworks (SOX, security governance).
 * Experience in multi-cloud or hybrid cloud environments.

Core Competencies:

Technical:

 * Predictive modeling
 * Time-series forecasting
 * Anomaly detection
 * AI automation
 * Data pipeline architecture
 * ML Ops

Leadership:

 * Strategic thinking
 * Cross-functional collaboration
 * Executive communication
 * Mentorship and team development
 * Ownership & accountability

Behavioral:

 * Data-driven decision making
 * Problem-solving mindset
 * Continuous learning
 * Innovation-driven

## Description

St. Louis, MO, United States (On-site)
Contract (3 months 17 days)
Published 16 hours ago
ML Platforms
ML Frameworks
mlops
data engineering
cost optimization
Python
AI Governance
AI automation
monitoring

 * We are seeking a highly skilled Data Scientist Lead to drive enterprise AI/ML strategy, predictive analytics, and intelligent automation initiatives across our large-scale database and cloud ecosystem. This role will lead the design and deployment of advanced machine learning models, AI-driven monitoring solutions, and data science frameworks that enhance performance, reduce risk, optimize costs, and enable data-driven decision-making.
 * The ideal candidate combines strong hands-on data science expertise with leadership capability, strategic thinking, and cross-functional collaboration experience., AI/ML Strategy & Leadership:
 * Define and execute the enterprise AI/ML roadmap aligned with business objectives.
 * Lead development of predictive maintenance, anomaly detection, and capacity forecasting models.
 * Establish best practices for ML lifecycle management (ML Ops).
 * Partner with Engineering, Cloud, Security, and Operations teams to embed AI into core platforms.

Advanced Analytics & Modeling:

 * Design, develop, and deploy machine learning models (regression, classification, clustering, time-series forecasting).
 * Implement predictive performance analytics for database infrastructure.
 * Develop cost optimization and workload forecasting models.
 * Leverage Generative AI for automation of operational tasks.

Data Engineering & Architecture Alignment:

 * Collaborate with database and cloud architects on scalable data pipelines.
 * Design data ingestion, feature engineering, and model training workflows.
 * Ensure data quality, governance, and compliance standards are met.

Team Leadership & Mentorship:

 * Lead and mentor a team of data scientists and ML engineers.
 * Drive cross-training and upskilling within Database Services.
 * Establish coding standards, documentation, and model validation processes.
 * Provide executive-level reporting and insights.

Operationalization & Governance:

 * Deploy models into production environments with monitoring and retraining pipelines.
 * Implement explainability and model validation frameworks.
 * Ensure AI governance, audit readiness, and ethical AI standards., This role will:
 * Improve infrastructure reliability through predictive insights
 * Reduce operational costs via AI-driven optimization
 * Accelerate modernization initiatives
 * Enhance compliance and risk management
 * Enable intelligent automation across database services


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