Lead Data Scientist

• Act as the engine room of Greyparrot's data methodology, transforming raw computer vision outputs into insights the industry can rely on. • Own the contracted Deepnest client analytics, the reports and data clients have paid for, shipped on time and to a high standard, while developing the statistical methodology that makes delivery scalable and trusted. • Report directly to the CTO with one direct report and scope to grow the team as the business scales.

• Bring 5+ years in data science working with large-scale, noisy real-world data in environments where data quality and fail modes are constant challenges. • Have a strong practical understanding of the implications of working with data derived from deep learning models, specifically the nuances of integrating computer vision outputs into broader statistical simulations. • Be proficient in Python and SQL, building analysis pipelines and getting to robust outputs independently without needing a data engineering team. • Have owned external deliverables such as reports or data products that clients or senior stakeholders have relied on, understanding what makes insight land versus what gets ignored. • Have built methodology and process where none existed, not just inherited and executed, being 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 set standards and give others structure to work within.

• Have a background in high-volume, complex real-world data industries such as sustainability, waste and circular economy, satellite and geospatial, weather forecasting, industrial IoT, or manufacturing. • Have experience building probabilistic or confidence-aware statistical modelling frameworks. • Have experience reconciling process flows and extrapolating across coverage gaps in physical-world data. • Have experience documenting and productionising statistical methodology for ML Ops teams to implement.

• Implement the next iteration of Greyparrot's statistical modelling framework, strengthening how the company reconciles process flows, extrapolates across coverage gaps, and quantifies confidence in outputs. • Ensure contracted Deepnest clients receive their analytical reports and insight outputs to a consistently high standard and on schedule with defensible methodology and actionable findings. • Build a documented framework of templates, quality standards, and methodology so output quality is repeatable and transferable rather than dependent 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 what gets prioritised on the research roadmap. • Manage and develop a Data Analyst direct report with scope to grow the team as the business scales. • Work toward a credible path for a confidence-aware, probabilistic foundation for the metric engine within 12 months.

• Work hybrid from the London office at least one day per week. • Report directly to the CTO. • Join a pioneering AI company tackling the global waste crisis. • Have scope to grow and build out the data science team as the business scales.

Greyparrot is on a mission to solve the global waste crisis by introducing transparency and automation to a traditionally opaque sector. As the pioneer of AI-powered waste intelligence, the company's technology processes billions of waste objects, turning trash into actionable data. Through a hardware-enabled SaaS model comprising the Greyparrot Analyzer and Deepnest packaging intelligence platform, the company empowers recycling facilities, FMCG giants, and global regulators to track packaging performance, maximise recovery, and turn circular economy aspirations into reality.