Lead Data Scientist

• Act as the "engine room" of Greyparrot's data methodology, transforming raw computer vision outputs into insights the waste industry can rely on. • Own the contracted Deepnest client analytics in the near term — the reports and data clients have paid for, shipped on time and to a high standard. • Develop the statistical methodology (the metric engine) that turns raw computer vision outputs into defensible, quantified waste metrics, making delivery scalable and trusted. • Report directly to the CTO with one direct report (a Data Analyst) and scope to grow the team, sitting alongside the Head of Data R&D who owns longer-term structural research. • Work with physical-world data — noisy, incomplete, and derived from computer vision models running in live recycling facilities — treating messy data as the problem to solve, not a reason to wait.

• Bring 5+ years in data science working with large-scale, noisy, real-world data, where data quality and failure modes are constant challenges. • Bring a strong practical understanding of working with data derived from deep learning models, especially integrating computer vision outputs into broader statistical simulations, knowing where a model can mislead you and how to account for it. • Build analysis pipelines in Python and SQL and reach robust outputs independently, without needing a data engineering team (production-ready output isn't expected). • Have owned external deliverables — reports or data products that clients or senior stakeholders relied on — and understand what makes insight land versus what gets ignored. • Have built methodology and process where none existed, comfortable setting standards and navigating ambiguity at pace. • Have managed or mentored at least one person, with a clear view of what good looks like and the ability to give others structure to work within.

• Bring a background in high-volume, complex real-world data industries such as satellite and geospatial, weather forecasting, industrial IoT, or manufacturing. • Have experience productionising statistical methodology alongside ML Ops teams. • Have worked with probabilistic or confidence-aware modelling frameworks. • Have built a data function from the ground up in a startup or scale-up.

• Advance the metric engine, implementing the next iteration of Greyparrot's statistical modelling framework to strengthen how process flows are reconciled, coverage gaps extrapolated, and confidence quantified, working toward a confidence-aware, probabilistic foundation. • Own contracted Deepnest deliverables end-to-end, ensuring analytical reports and insight outputs ship on time and to a consistently high standard, with defensible methodology and no surprises at delivery. • Build a repeatable delivery framework — templates, quality standards, and documented methodology — so output quality doesn't depend on starting from scratch each engagement. • Provide a reliable feedback loop to the Head of Data R&D on which model outputs translate to client value, shaping the research roadmap. • Document the methodology so it's defensible, transferable, and ready to be productionised by ML Ops. • Lead and mentor a Data Analyst, setting standards and growing the team as the business scales.

• Own Greyparrot's data methodology as the "engine room" of the business, reporting directly to the CTO. • Take real ownership from day one, with the freedom to decide how to achieve the outcomes you're held to. • Lead and grow a team, with scope to expand as the business scales. • Work on a mission to increase transparency and automation in waste management and accelerate the circular economy.

Greyparrot is a London-based leader in AI waste analytics, on a mission to digitise waste flows and accelerate the circular economy. Its camera systems and AI software are deployed in recycling plants and waste facilities worldwide, generating granular, real-time waste composition data across facilities and turning it into insight that clients can trust and act on. The company's Analyzer units track and identify materials passing through recovery facilities, while its Deepnest platform gives brands direct access to how their packaging performs at end of life. By bringing transparency and accountability to a sector where waste data has been largely non-existent, Greyparrot is tackling a global waste crisis in which recycling rates remain around 10%.