You will work within a data-driven organization. You will be responsible for developing, improving, and deploying predictive models that directly contribute to better planning and decision-making. You will collaborate closely with data engineers, analysts, and business stakeholders to translate predictions into concrete value for the organization. You will join a modern organization where data plays a central role in daily operations and strategic choices.
You will work in an experienced team of data and ML specialists focused on reliable and scalable solutions with a direct impact on processes and customers. The culture is professional, open, and focused on continuous improvement.
What you do
- Develop, train, and optimize forecasting models (e.g., demand, volume, and capacity forecasts)
- Translate business problems into reliable and scalable ML solutions
- Build and manage end-to-end ML pipelines, from data processing and feature engineering to deployment and monitoring
- Apply and combine statistical time series methods and machine learning techniques
- Monitor model performance, forecast accuracy, and data drift in production
- Implement CI/CD and automation around model lifecycle management
- Clearly communicate forecasts, uncertainties, and model choices to non-technical stakeholders
- Actively contribute to knowledge sharing and technical innovation within the team
Profile
As a Machine Learning Engineer, you combine strong knowledge of forecasting and time series analysis with solid engineering skills. You are curious, analytical, and pragmatic: you know when a simple model suffices and when complexity pays off. You feel comfortable bridging technology and business and actively contribute to the application of your models.
What they ask
- Demonstrable experience with developing and deploying forecasting models
- Minimum 3 years of experience as an ML Engineer, Data Scientist, or in a similar role
- Excellent Python skills (e.g., pandas, scikit-learn) and solid software engineering principles
- Knowledge of time series methods such as ARIMA, Prophet, gradient boosting (XGBoost/LightGBM) or deep learning variants
- Experience with SQL, version control, and automated workflows
- Experience with cloud platforms (Azure, AWS, or GCP)
Desirables.
- Experience with MLOps tooling such as MLflow, Databricks
- Knowledge of probabilistic forecasting and quantifying uncertainty
- Experience with hierarchical forecasting or forecasting on large numbers of time series
- Experience with distributed processing frameworks such as Apache Spark
- Knowledge of Infrastructure-as-Code (e.g., Terraform)
How to apply
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