UK-based | Mostly remote | Salary starting at £70,000 + 0.5–1% equity
What can the information companies leave online tell us about what they’ll do next?
We’re working with an early-stage startup exploring that question. Its product collects information from public sources and connects it across businesses, people and events. They’re developing their proprietary prediction capability, testing which patterns provide useful clues about future business activity.
They’re hiring their first dedicated research engineer, joining the CTO and founding engineer as the third person in the technical team.
You’ll shape the research plan alongside the CTO, design experiments and develop models that can be used in the product.
That means connecting historical signals with subsequent outcomes, developing the knowledge graph behind the system, and testing whether new approaches improve prediction quality.
Applied machine learning and data science are the main focus, supported by knowledge graphs and LLM systems. You’ll also contribute to features and help solve wider engineering problems as they arise.
Experience predicting rare or low-frequency events would be useful. Fraud, risk, churn and forecasting are all relevant examples. Neo4j, previous startup experience and broader full-stack engineering would also be welcome.
Interested? Send me your CV and, if you have one, a project or research link that shows the kind of work you’d bring to the role.
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