Job Description

Key Responsibilities

Own the feature engineering roadmap for ETA & Destination Forecast across all 4 commodity types — propose and implement new features as dbt models using Airflow to orchestrate the data pipelines, and validate their impact through structured experiments.
Design and run experiments using kpler-ml framework, logging all runs from train to evaluation to MLflow and producing structured comparison reports against the production baseline before any promotion.
Work directly with Commodities Market Analysts and product stakeholders to understand where prediction quality matters most commercially — and use that to prioritise the experiment backlog.
Contribute to the drift monitoring setup — validate PSI/KS thresholds using MLFlow against historical inference batches; define what constitutes a meaningful drift signal for PE and DF specifically.
Document experiment decisions in MLflow and Confluence documents — the experiment history is a first-class artifact, not an afterthought.

Experience & Background

2+ years applying ML to real-world production problems — not research or hackathon work, but models running in production with real consequences for errors

Experience with geospatial or sequential data — vessel trajectories, routing patterns, H3/S2 grid systems, or equivalent spatial representations

Python proficiency at a level sufficient to implement new features, write dbt models, and script experiments — not just use notebooks

Familiarity with MLflow or equivalent experiment tracking (Weights & Biases, Neptune, etc.)

Desirable:

Domain knowledge of maritime shipping, commodity trading, or cargo intelligence — understanding what a port call sequence or a vessel's draught profile means physically, not just statistically

Familiarity with Redshift or columnar warehouses for large-scale feature queries and dbt (authoring or reading SQL models)