Anomaly Detection for Water & Energy Consumption

SeafaringIT

StageSur site4 à 6 moisDate limite : 23 févr. 2026
Data Science & Machine LearningData Analysis / Data ScienceAnomaly detectionExplainabilityMLOps / DevOpsAPIs & Integrations

Postuler

Description

  • Goals:
  • Build an intelligent anomaly detection system to identify water leaks and energy overconsumption
  • Provide explainable insights into anomaly causes
  • Prioritize interventions by impact and savings
  • Student roles: Machine learning engineers, data scientists, backend engineers, MLOps specialists
  • Expected outcomes: ML-powered anomaly detection with explainability, API exposure, model tracking, and impact analysis
  • Key features:
  • Dataset construction (open/synthetic) with feature engineering
  • Multiple anomaly detection models (statistical + ML) with benchmarking
  • Explainability layer (SHAP) and error analysis
  • Alert API with scoring and prioritization
  • Model tracking/versioning with MLflow
  • Concept drift monitoring and performance tracking
  • Business impact report (savings, environmental impact)
  • Technologies: Python, scikit-learn, XGBoost, Isolation Forest, SHAP, MLflow, FastAPI, PostgreSQL, Elasticsearch, visualization tools