Principal Data Scientist (Hiring Immediately)

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.

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 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.

As a Principal Data Scientist, you will become a key part of Data Platform Team to 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 collaborate closely with software engineers, data scientists, and domain experts to translate cutting‑edge research into operational, trading‑ready intelligence.

Requirements

You Are

• A demonstrably strategic, high-impact and experienced individual contributor capable of leading complex projecs across Data Science, Machine Learning, and AI, including hands‑on work building and deploying production-grade ML models,

• Deeply grounded in the theoretical and mathematical foundations of ML/AI and well‑versed in current research and emerging methodologies,

• PhD-educated in a quantitative field such as Computer Science, Statistics, Applied Mathematics, Physics, or a related discipline,

• Skilled at translating advanced research concepts into practical, high‑impact industrial applications,

• Fluent in Python and experienced in regression and classification modelling, clustering, time‑series analysis, anomaly detection, sequence‑to‑sequence architectures, and stochastic optimisation,

• Experienced across the full ML lifecycle: experiment design, model development, validation, deployment, monitoring, and long‑term maintenance,

• Motivated by intellectually rigorous collaboration with energy analysts, traders, and technologists, and comfortable engaging in constructive technical debate,

• Energised by complex, real-world challenges and committed to bringing innovative ML approaches into production environments,

• Passionate about mentoring and elevating colleagues, helping them strengthen their ML engineering capabilities and grow their careers.

Awesome if you

• Have experience in the energy sector or a strong understanding of energy systems and operational dynamics,

• Have exposure to quantitative trading, including arbitrage, strategy development, backtesting, and risk management for physical or derivative assets,

• Have practical experience with AWS services and cloud‑native infrastructure,

• Are familiar with modern MLOps tools and frameworks and can partner effectively with Data and ML engineers to deploy scalable, reliable, real‑time inference pipelines

Benefits

• Enjoy flexible hybrid working – split your time between home and our office, with the freedom t]]>