Machine Learning Engineer (Time-Series & Decision Systems)
Location: Porto (On-site)
We are building a next-generation decision intelligence platform for industrial markets.
Industrial companies operate in highly complex environments influenced by thousands of external signals—commodity prices, trade flows, energy markets, weather, geopolitics, logistics constraints, and shifting demand. Yet, many decisions are still made using spreadsheets and intuition.
Our platform identifies the most relevant signals across billions of time-series data points and transforms them into actionable, decision-ready insights.
This role sits at the intersection of machine learning, economics, and complex systems, focused on solving real-world decision-making problems under uncertainty.
What You'll Work On
Time-Series Modelling at Scale
Commodity and chemical price dynamics
Supply chain demand signals
Trade flow shifts and macro indicators
Energy and logistics cost dynamics
Production and consumption cycles
Modelling Techniques
ARIMA, ETS, and state-space models
Machine learning models for time-series
Multivariate signal discovery
Regime detection and structural breaks
Probabilistic forecasting
Signal Discovery
Identify predictive relationships across datasets
Detect causal drivers of market behavior
Filter noise from large-scale data
Decision-Grade Forecasting
Define decision thresholds
Build uncertainty ranges
Scenario simulations
Early warning systems
Production Systems
Feature engineering pipelines
Scalable ML workflows
Backtesting frameworks
Model monitoring and evaluation
What We're Looking For
Must Have
Strong Python skills (NumPy, pandas, scikit-learn, PyTorch or JAX)
Solid experience in time-series modelling
Strong problem-solving and analytical thinking
Good to Have
Forecasting methods (ARIMA, Prophet, etc.)
Causal inference / modelling
Experience with large-scale datasets
SQL, cloud platforms, and data pipelines