ML Engineer – Forecasting, Time Series and Mathematical Optimization

• 💰 Salary: up to €40,000 gross per year.

ML Engineer - Forecasting, Time Series and Mathematical Optimization

📍 Location: 100% remote
🏢 Client: Santander
💰 Salary: Up to €40,000 gross per year
📄 Project: Cash management and demand forecasting in the banking sector.

We are looking for a Machine Learning Engineer to join a strategic project in the banking sector, oriented towards demand forecasting and optimization of cash management.

The selected person will participate in the analysis, development, validation, and improvement of traditional Machine Learning models, especially time series, forecasting, and mathematical optimization models applied to operational and business problems.

A solid foundation in predictive modeling, model training, and evaluation is required, as well as optimization knowledge to improve decision-making. They will also participate in the adaptation and productionization of models in corporate environments.

• Analyze, develop, train, and validate supervised Machine Learning models.

• Work with time series and forecasting models for demand forecasting.

• Evaluate and compare different modeling approaches, analyzing accuracy, stability, and predictive capacity.

• Optimize forecasting models and propose improvements in algorithms and training strategies.

• Work with mathematical optimization models applied to resource allocation, planning, and operational decision-making.

• Analyze denomination optimization and cash management problems.

• Evaluate the combination of predictive models with optimization algorithms and business rules.

• Participate in the design and improvement of model training, inference, and retraining processes.

• Collaborate in the adaptation of models to production environments using MLOps best practices.

• Work with analytical platforms and distributed environments such as Azure Databricks and PySpark.

• Document models, experiments, results, and technical decisions.

• 🏠 Modality: 100% remote.

• 🏦 Project in the banking sector with Santander.

• 🚀 Participation in a Machine Learning project applied to demand forecasting and operational optimization.

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• Python, Scikit-learn, LightGBM.

• Prophet, StatsForecast, MLForecast, or Nixtla.

• Regression and supervised learning models.

• Time series and demand forecasting models.

• Mathematical programming.

• Constraint optimization.

• Heuristics and optimization algorithms.

• Operations research and decision optimization.

• Python, SQL, PySpark, and Apache Spark.

• Azure Databricks.

• MLflow.

• Git and CI/CD tools.

• Analytical skills and a problem-solving mindset.

• Technical judgment to evaluate and improve models.

• Autonomy and capacity for learning.

• Communication with technical and business teams.

• Results orientation and continuous improvement.