Lead Data Scientist

• Own and advance Greyparrot's statistical modelling framework (metric engine) to transform raw computer vision outputs into defensible, quantified waste metrics.
• Deliver contracted Deepnest client analytics and reports on time and to a high standard, ensuring methodology is defensible and findings are actionable.
• Build a repeatable, documented framework for insight delivery so quality does not depend on starting from scratch each engagement.
• Provide a feedback loop between R&D and delivery to ensure model outputs translate to client value.

This role directly increases transparency and accountability in waste management, enabling recycling targets to be upheld, reducing ocean dumping, and accelerating the circular economy. By turning raw data into trusted insights, it helps cut the $1.5 trillion annual cost of plastic pollution.

• 5+ years in data science working with large-scale, noisy real-world data (e.g., satellite, geospatial, industrial IoT).
• Strong practical understanding of deep learning model outputs, especially computer vision, and how to account for their limitations.
• Proficiency in Python and SQL for building analysis pipelines independently.
• Experience owning external deliverables (reports or data products) for clients or senior stakeholders.
• Experience building methodology and process from scratch, and managing or mentoring at least one person.

Compensation and perks not explicitly stated. The role offers the opportunity to work on a mission-driven problem with a team of experts, reporting to the CTO, with scope to grow as the business scales.

Greyparrot's mission is to digitize waste flows to accelerate the circular economy. They deploy camera systems and AI software in recycling plants worldwide to measure material flows and provide waste analytics, aiming to solve the global waste crisis.