Lead Research Scientist - Computational Economics & Temporal Network Intelligence

About the role

Gritnord is building a B2B matchmaking platform that connects companies with genuine commercial reasons to meet, using complex, continuously changing business data. It is looking for a Lead Research Scientist to build the scientific intelligence behind the platform, working directly with the leadership and engineering teams. The role is described as the first team research position and is open to Europe, with a hybrid setup.

Key responsibilities

• Investigate how purchasing needs, commercial opportunities and business decisions emerge.
• Research complex correlations, dependencies, event sequences and relationships across changing business environments.
• Develop novel approaches to temporal reasoning, predictive intelligence and network-based modeling.
• Explore how intent, willingness, timing and decision-making influence commercial opportunities.
• Develop and test intelligent buyer-supplier recommendation and matchmaking models.
• Experiment with statistical methods, machine learning and LLMs, and validate hypotheses against real commercial outcomes.
• Work with engineers to turn scientific discoveries into scalable product capabilities.

Required profile

• Strong scientific background in Data Science, Applied Mathematics, Statistics, Computer Science, Physics, Economics or a related field.
• Demonstrable track record of developing, testing and validating predictive models or original research approaches.
• PhD or research-intensive master's degree is a strong advantage; proven research outcomes and measurable results matter most.
• Ability to distinguish meaningful discoveries from coincidental patterns and to challenge established methods.

Required skills

• Machine learning, statistical inference, probability and causal reasoning.
• Hands-on work with data, algorithms and scientific computing.

What we offer

• A leading role with ownership of the scientific development and direct collaboration with leadership and engineering.
• Freedom to explore unconventional ideas and access to real commercial challenges and growing datasets.
• Career progression based on impact, with the potential to lead the future research function.
• Stock options and a competitive gross salary depending on experience and demonstrated capabilities.

To apply, send your CV, relevant research or projects and a brief account of one complex problem you solved, with a link to a recent successful project if possible. Selected candidates will complete a short practical research challenge.