Posted on: 08/04/2026
Description :
Responsibilities :
- Design, develop, and deploy machine learning models for predictive analytics, optimization, and decision support
- Build end-to-end ML pipelines including data ingestion, feature engineering, model training, validation, and monitoring
- Collaborate with data architects to ensure scalable, secure, and compliant data and ML architectures
- Apply statistical analysis and advanced ML techniques (supervised, unsupervised, time series, NLP where applicable)
- Optimize model performance and reliability for production environments
- Partner with business stakeholders to communicate insights, assumptions, and model limitations clearly
- Ensure adherence to data governance, cybersecurity, and privacy standards
- Support deployment using CI/CD and MLOps best practices
- Mentor junior engineers and contribute to technical standards and reusable frameworks
Requirement :
- Strong proficiency in Python / R , ML libraries (scikit learn, TensorFlow, PyTorch, XGBoost, etc.)
- Solid understanding of statistics, probability, and model evaluation techniques
- Experience working with structured and unstructured data at scale
- Proven ability to deploy ML models into production environments
- Strong problem solving, communication, and stakeholder engagement skills
Preferred Qualifications :
- Masters degree in Data Science, AI, or a related field
- Experience in EPC, engineering, construction, manufacturing, or asset intensive industries
- Exposure to time series forecasting, anomaly detection, or optimization models
- Experience with cloud platforms (Azure preferred)
- Familiarity with MLOps tools and practices
- Experience working in global, matrixed organizations
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