Strategic Projects Lead

The role

As a Strategic Projects Lead, you'll fully own and optimize the data annotation and machine learning workflows behind Encord's largest client relationships. You'll work directly with clients, annotation specialists, ML engineers, Account Managers, and Forward Deployed Engineers to ensure the data powering their models is fast, accurate, and scalable.

This is a hands-on, high visibility role. You have direct influence on whether Encord's highest-value customers renew and expand.

What you'll do

• Own data annotation projects end-to-end, translating complex AI/ML requirements into clear workflows and instructions for annotation teams
• Design and refine annotation processes, audit results, and build feedback loops that raise data quality
• Act as a trusted advisor to clients - designing and implementing the human-annotation workflow that gets them to production fastest
• Partner with product and engineering to drive improvements in AI training data tools and methodology
• Directly influence account health: your workflow design and execution are a primary driver of whether strategic accounts renew, expand, or churn

Who we're looking for

• A sharp, execution-oriented operator with a consulting or AI-company pedigree. A structured thinker, strong PM instincts, bias for getting things done
• Analytically rigorous and comfortable with ambiguity. You break down operational problems from first principles
• Technically fluent: comfortable querying a database, auditing annotation outputs, or automating a workflow in Python
• A natural translator between ML engineers and non-technical clients on multi-stakeholder projects
• Entrepreneurial - you take ownership without waiting to be told what to do

Experience requirements

• 3-7 years of professional experience, with a strong preference for backgrounds in top-tier strategy consulting and/or operations or data roles at leading AI or technology companies
• Proven ownership of complex, multi-stakeholder workflows end-to-end: scoping, execution, QA, iteration
• Experience designing/optimizing data operations with an eye for quality, consistency, and scalability, ideally human-in-the-loop or structured labeling work
• Demonstrated ability to engage effectively with both technical stakeholders (ML engineers, data scientists) and non-technical clients, translating requirements clearly in both directions
• Bonus: hands-on experience with computer vision, generative AI, or multimodal data workflows; prior exposure to data annotation platforms or quality management frameworks; experience coaching or managing operational teams
• Bonus: Working proficiency in Python or SQL, with the ability to query data, automate workflows, or audit annotation outputs; broader familiarity with relational databases or data annotation tooling equally valued