Description

You will own the end-to-end development of machine learning models that improve process performance and stability.

Responsibilities

• Design, train, and validate machine learning models for concrete batching operations.
• Lead model development from data exploration and feature engineering through deployment.
• Define and implement strategies for model retraining and validation to ensure continuous improvement.
• Integrate models into production systems running on Azure or AWS environments.
• Document methodologies, experiments, and decisions to maintain reproducibility.

Required Skills

• 5+ years of hands-on experience in applied machine learning, focusing on regression, forecasting, or optimization.
• Proven experience building production-grade ML pipelines from experimentation to deployment.
• Strong proficiency in Python and relevant data science libraries, writing clean, production-ready code.
• Expertise with modern development practices: Git, code review, automated testing, and CI/CD pipelines.
• Solid understanding of data lifecycle management, including time-series feature engineering.
• Experience applying ML model monitoring, versioning, and continuous retraining frameworks.
• Familiarity with cloud ML ecosystems, specifically Azure or AWS.
• Strong grasp of data science concepts like cross-validation and quantile modeling.
• Ability to document and present analytical findings to technical and non-technical audiences.

Key Skills

Education

Any Graduate