Jobs / United Kingdom / Vortexa

Vortexa

Principal Data Scientist

London, England, United Kingdom Posted 1 month ago

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BudgetingData engineeringMicroservicesMachine learningTranslation

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Sponsorship verdict

Sponsorship possible

One solid signal, not two. Worth applying, and worth asking about sponsorship early.

  • Employer is on a government sponsor recordThis employer holds a UK sponsor licence (Skilled Worker, A rating).
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  • Can’t check pay against the visa rulesNo salary stated. UK Skilled Worker visa needs at least £41,700 a year (new-entrant (under 26, recent Student/Graduate visa) or STEM PhD: £33,400).
  • Confirmed live todayWhen a source last listed this job as open.
Details and sources
Employer is on a government sponsor record
This employer holds a UK sponsor licence (Skilled Worker, A rating). Source: Register of licensed sponsors (UK Home Office).
Can’t check pay against the visa rules
No salary stated. UK Skilled Worker visa needs at least £41,700 a year (new-entrant (under 26, recent Student/Graduate visa) or STEM PhD: £33,400). Source: https://www.gov.uk/skilled-worker-visa/when-you-can-be-paid-less, rules effective 2025-07-22.

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About the role

Vortexa is a fast-growing international technology business founded to solve the immense information gap that exists in the energy industry. By using massive amounts of new satellite data and pioneering work in artificial intelligence, Vortexa creates an unprecedented view on the global seaborne energy flows in real-time, bringing transparency and efficiency to the energy markets and society as a whole. http://www.vortexa.com/ Ingesting data from multiple external vastly different sources at hundreds of rich data points per second, moving terabytes of data while processing it in real time, running complex and complicated prediction and forecasting AI models while coupling their output into a hybrid human-machine data refinement process and presenting the result through a nimble low-latency SaaS solution used by customers around the globe is no small feat of science and engineering. This processing requires a unique fusion of humans and machines, close collaboration between and deep expertise from data analysts, data scientists, industry experts and the end users. Vortexa's Data Platform, designed, developed and maintained by the Data Production Team, is a cloud-native ecosystem that powers the full lifecycle of our data and intelligence products. It integrates large-scale data pipelines, machine-learning models, AI agents, human-in-the-loop systems, and microservices to collect, process, connect, and govern global energy-flow data at scale. This platform underpins analytics, operational workflows, and real-time decision-making across the company. Our models ingest and interpret a diverse range of data, from satellite imagery and sensor feeds for millions of energy assets to unstructured commercial and operational shipping data such as customs filings, fixtures, and SPAs. These inputs drive predictive systems that support energy-demand forecasting, anomaly detection, and real-time recommendations for physical and derivative trading. What You'll Be Doing As a Staff/Principal Data Scientist, reporting to Vortexa's VP of Data Production, you will be the subject-matter authority for data science - the person the company turns to on the problems no one has cracked yet - and you will play a central role in designing, implementing, and deploying advanced AI/ML methodologies and production-grade systems. Your work will be held to the scrutiny of energy analysts, traders, operations teams and regulatory stakeholders, and must meet the performance, reliability and robustness standards required for critical energy infrastructure. You will be: • Working on frontier problems: where there is no obvious baseline, benchmark or established definition of success. You will be expected to define what good looks like, and bring the rigour needed to reach a meaningful conclusion. • Raising the ceiling on models already in production: working closely with the pods that own to make live predictions measurably better. • Taking problems end-to-end: moving across pods and owning substantial projects from initial exploration all the way through to long-term maintenance, ultimately delivering robust, production-grade solutions. • Turning research into impact: identifying emerging approaches, formulating hypotheses, designing rigorous experiments, evaluating new techniques and translating research into practical solutions that work in the real world. • Building the capability of Data Science: through technical review, pairing and mentoring, setting the standards for how we work, and by being the person analysts, engineers and product managers bring their hardest questions to. • Leading the Data Science Guild: setting the technical agenda and creating the forum where the significant questions across the practice are surfaced, challenged and worked through

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