ml engineer demand forecasting

Fetcherr builds responsible AI that transforms market complexity into measurable profit growth. Its proprietary Market Model delivers granular demand predictions and real-time decision intelligence for commercial teams, using transparent glass-box architecture and market data rather than personal data. The technology was first deployed in global aviation and scales across volatile markets, delivering an average profit uplift of 7%.

• Collaborate with cross-functional teams to keep ML systems robust, explainable, and aligned with business needs;
• Monitor and report on ML model performance, reliability, and explainability metrics;
• Participate in model retraining procedures and automate and optimize MLOps pipelines;
• Extend and scale monitoring pipelines, including support for new features in development;
• Investigate, troubleshoot, and resolve issues in production ML workflows from initial triage to root-cause analysis with model owners;
• Develop and maintain repositories for feature engineering, inference monitoring pipelines, and artifact monitoring tools;
• Perform exploratory data analysis on historical datasets to identify quality issues and maintain data health;
• Implement and oversee production-based adjusters across customer deployments;
• Evaluate and track critical ML artifacts, including explainability files, coverage metrics, and feature alignment;
• Support the development and maintenance of internal tools, including interfaces, registries, and feature monitoring frameworks;
• Build and maintain static and temporal features, including seasonality, event-based, and price-related features.

• 5+ Years of hands-on experience in data science, ML operations, or applied ML support;
• Proficiency in Python and standard data and ML libraries, including Pandas/Polars, NumPy, Scikit-learn, and SQL;
• Strong data visualization and exploratory data analysis skills for monitoring and debugging pipelines;
• Experience with time-series data and feature engineering;
• Familiarity with explainability tools and model monitoring best practices;
• Strong problem-solving skills across data, code, and model workflows;
• Excellent communication skills for summarizing findings to technical and non-technical audiences;
• Experience with cloud-based ML platforms, preferably GCP;
• Familiarity with Docker, K8s, CI/CD workflows, or ML observability tools;
• Bachelor's or Master's degree in Computer Science, Machine Learning, Statistics, Engineering, or a relevant field;
• Nice to have: Experience with PyTorch or TensorFlow, familiarity with Airflow, Kedro, or Dagster, prior exposure to demand forecasting, pricing, or revenue management.

• No conditions specified