Data Scientist

Job summary

At Kpl***, we are dedicated to helping our clients navigate complex markets with ease. By simplifying global trade information and providing valuable insights, we empower organisations to make informed decisions in commodities, energy, and maritime sectors. Since our founding in 2014, we have focused on delivering top-tier intelligence through user-friendly platforms.

Qualifications

• Join us to leverage cutting-edge innovation for impactful results and experience unparalleled support on your journey to success.
• You are not handed a Jupyter notebook and a dataset.
• Don’t let the confidence gap stand in your way, we’d love to hear from you!
• We understand that experience comes in many different forms and are dedicated to adding new perspectives to the team.
• Kpl*** is committed to providing a fair, inclusive and diverse work-environment.
• We believe that different perspectives lead to better ideas, and better ideas allow us to better understand the needs and interests of our diverse, global community.
• We welcome people of different backgrounds, experiences, abilities and perspectives and are an equal opportunity employer.
• By applying, I confirm that I have read and accept the Staff Privacy Notice
• Find more English Speaking Jobs in United Kingdom on Arbeitnow

Responsibilities

• Design and run experiments using Kpl***-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)

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