QC Team Lead – Budapest

Who we are?

As the global pioneer for digital twins of urban forests, greehill provides highly detailed digital copies of each individual tree in a city and thereby supports the digital transformation of smart cities world-wide. Combining the latest laser scanning technology with the most advanced AI & machine learning methods, greehill enables cities to be radically more productive in their daily operations and to improve the well-being of citizens through a greener, healthier, and more resilient urban environment. We currently operate offices in Budapest, Hannover, Singapore, San Francisco, and Paris.

Overview

We are looking for a QC Team Lead to lead greehill’s Quality Control function. The position is based in Budapest, Hungary, within our Operations / Data Production team, and is offered as a full-time, 1-year fixed-term contract.

As QC Team Lead, you will lead a small, focused team of greehill’s QC function, including support. You will ensure that data delivered through our production workflows meets quality standards, project requirements and client expectations.

Your primary objective will be to build a scalable, AI-native QC system that allows production volume to grow without a proportional increase in manual quality control. The system will combine automated validation, risk-based sampling, AI-assisted issue detection and targeted human review.

This is a hands-on player-coach role combining people leadership, quality-system design, data quality analysis and process improvement.

Your mission will be:

Team Leadership

· Lead and develop the three-person QC function, including workload planning, prioritization and performance management.

· Define daily and weekly QC priorities based on project deadlines, production capacity and quality risk.

· Train and coach QC specialists, set clear expectations for quality and productivity, and build resilience through cross-training and documented working practices.

· Ensure that complaints, QC findings and production issues are handled through one consistent quality process.

· Maintain consistent QC practices across locations, projects and production teams.

Quality System Design

· Own the end-to-end QC process for greehill’s data-production workflows.

· Design and implement an AI-native QC system embedded in the production flow.

· Define automated checks, sampling strategies, acceptance criteria, quality thresholds, quality gates and escalation procedures.

· Use risk-based sampling to focus human review on high-risk and ambiguous cases.

· Define which cases can be accepted automatically, rejected automatically or routed for human review.

· Work with ML and Engineering teams to improve validation rules, issue detection and automated quality checks.

Data and Performance Monitoring

· Establish and maintain QC KPIs, quality dashboards and a consistent defect taxonomy.

· Track error rates, escaped defects, client complaints, completeness, accuracy, rework, rejection rates, review time and automation coverage.

· Analyze QC results to identify trends and root causes.

· Distinguish isolated operator errors from systematic problems in processes, models, tooling or source data.

· Monitor the performance of automated checks, including false acceptance and false rejection.

Continuous Improvement

· Develop and maintain QC SOPs, checklists, quality standards and corrective-action processes.

· Turn QC findings and client complaints into structured feedback for Production, ML, Product and Engineering.

· Work with ML teams to identify model weaknesses and provide structured examples for model improvement.

· Lead root-cause analysis and corrective and preventive actions for recurring quality problems.

· Reduce repetitive manual inspection while maintaining or improving quality.

Project Quality Ownership

· Support project teams in defining project-specific quality requirements.

· Monitor quality throughout the lifecycle of high-priority projects and flag risks early.

· Provide final QC input before project delivery and ensure that lessons learned improve future workflows.

What Success Looks Like

· greehill increases production volume without compromising data reliability or requiring a proportional increase in manual QC capacity.

· Quality is consistent across all production teams and locations.

· Quality standards are clear and measurable, and quality decisions are traceable.

· A growing proportion of production is covered by automated quality checks.

· Human review is focused on high-risk and ambiguous cases.

· Escaped-defect, rework and client-complaint rates decrease.

· Production and model issues are detected and resolved faster.

· Feedback from QC and complaints flows effectively into ML, Product and Engineering.

· The QC function scales to support significant volume growth.

· Errors are prevented rather than simply detected after the fact.

We are looking for someone with:

· 3+ years of experience in Quality Control, Quality Assurance, Data Quality, Operations or a related field.

· Proven experience leading a small QC, QA, data-quality or operations team.

· Experience working as a hands-on lead who combines people management with individual delivery.

· Demonstrated success improving measurable quality outcomes in a production environment.

· Experience designing or implementing automated QC, data-validation or exception-handling workflows.

· Practical experience analyzing data with SQL, Python or comparable tools.

· Experience working with large datasets and identifying patterns in quality issues.

· Experience creating and improving SOPs, quality standards and operational processes.

· Experience investigating systemic defects and implementing corrective and preventive actions.

· Ability to prioritize quality issues based on business and client impact.

· Strong communication skills and ability to work across technical and operational teams.

· Professional English communication skills.

We value if you have

· Experience applying AI or machine-learning methods to anomaly detection, classification, prioritization or quality review.

· Experience with human-in-the-loop AI or confidence-based review workflows.

· Experience with geospatial data, GIS, LiDAR, photogrammetry or remote sensing.

· Experience working with distributed or outsourced production teams.

· Experience with data-quality dashboards and KPI reporting.

We offer

· Opportunity to join a dynamically growing startup team.

· Possibility to work and learn in an international, open-minded team of passionate (green-)tech experts.

· Ownership and impact.

· Exposure to cutting-edge AI and LiDAR technology.

· Contribution to urban sustainability and climate resilience.

Why greehill?

greehill combines urban forestry expertise, LiDAR, AI, and digital twin technology to help cities better understand and manage their urban forests.

This role offers the opportunity to build an AI-native quality system from the ground up, lead and develop a team, and directly shape the reliability of the data that cities rely on to make better-informed decisions about their urban forests.

Please note, our office attendance is high, with employees working on-site four days a week. The position is based in Budapest /Hungary/ so relocation might be needed from your side.