Job Summary

What You'll Do
- Train, evaluate, and iteratively improve geospatial classification and regression models built on existing deep learning architectures using multi-spectral satellite time-series data.
- Analyze model performance across diverse geographies, seasons, and target classes, diagnosing failure modes and adjusting training configurations, sampling strategies, and hyperparameters to drive meaningful improvements.
- Design and execute experiments to validate modeling decisions, interpret results, and translate findings into actionable next steps.
- Collaborate cross-functionally with product managers, domain scientists, and engineering teams to translate use cases into robust ML solutions.
- Operate independently within an existing ML infrastructure stack, running training jobs on Vertex AI, managing data in BigQuery and Cloud Storage, and deploying models to production endpoints.
- Work with an engineering mindset to improve existing ML frameworks and infrastructure as needed to support new product needs.




- Implement active learning and automated QA workflows to continuously improve quality and model performance.
- Manage the full model lifecycle, including experiment tracking, model registration, versioning, and deployment to AI Platform endpoints.
- Contribute to data pipeline improvements and orchestration workflows where needed to unblock modeling work.

What We're Looking For
- 4-6 years of experience in ML engineering or data science, with at least 2 years working with remote sensing or geospatial data.
- Demonstrated ability to work independently, owning a modeling problem end-to-end, unblocking yourself, and delivering results with mínimal hand-holding in a fast-moving environment.
- Strong intuition for model diagnostics, including interpreting training curves, confusion matrices, data leakage, and evaluation metrics to identify what's working and what isn't.
- Hands-on experience training deep learning models (TensorFlow or PyTorch) on

📌 Ml Data Scientist (Buenos Aires)
🏢 ALCOR
📍 Buenos Aires