Human Data Operations Strategist at Encord

London, England, United Kingdom -

Full Time

Start Date

Immediate

Expiry Date

16 Aug, 26

Salary

0.0

Posted On

18 May, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Annotation, Project Management, Python, SQL, Machine Learning Workflows, Stakeholder Management, Data Quality Assurance, Operational Strategy, Analytical Thinking, Computer Vision, Generative AI, Multimodal Data Workflows, Process Optimization, Client Advisory, Technical Communication, Database Querying

Industry

Software Development

Description

About us Encord is the universal data layer for AI that helps 300+ AI teams train and run models on the right data. Our platform indexes, curates, annotates, and evaluates data across the full AI lifecycle, from development through production. Trusted by Woven by Toyota, AXA, UiPath, Zipline, and more. We're an ambitious team of 100+ working at the frontier of AI and have raised $60M in Series C funding from Wellington Management, CRV, Next47 and Y Combinator. The role As a Human Data Operations Strategist, you will play a critical role in managing and optimising data annotation and machine learning workflows for our clients. You will work closely with cross-functional teams, including clients, annotation specialists, and machine learning engineers, to ensure high-quality data is available for AI models. \n What you'll do Oversee data annotation projects, translating complex AI and machine learning requirements into clear workflows and instructions for data annotation teams Ensure the highest standards of data quality by designing and refining annotation processes, auditing results, and implementing feedback loops Act as a trusted advisor to clients, helping them design and implement the best data annotation workflow for their human annotation process Provide guidance and feedback to the annotation team, ensuring team members are equipped with the context and skills needed to perform high-quality work aligned with project requirements and best practices Work closely with product and engineering teams to drive improvements in AI training data processes, tools, and methodologies Who we're looking for A sharp, execution-oriented operator with a consulting or AI company pedigree — you bring structured thinking, strong project management instincts, and a bias for getting things done Analytically rigorous and comfortable with ambiguity — you break down complex operational challenges from first principles and build clear, actionable plans to solve them Technically fluent enough to get hands-on with data — whether that's querying a database, auditing annotation outputs, or automating a workflow in Python Passionate about AI and machine learning, with genuine curiosity about how data quality and operations underpin model performance A natural communicator who can translate fluidly between ML engineers and non-technical clients, keeping complex multi-stakeholder projects on track Entrepreneurial and collaborative — you thrive in fast-paced environments and 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 ability to own complex, multi-stakeholder workflows end-to-end — from scoping and planning through execution, quality assurance, and iteration 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 Experience designing or optimising data operations processes with a strong eye for quality, consistency, and scalability — ideally in a context involving human-in-the-loop workflows or structured labelling tasks 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 Why Encord Competitive salary, commission, and equity in a high-growth start-up Strong in-person culture — most of the team works from our London office 4+ days/week 25 days annual leave + UK public holidays Annual learning & development budget Travel for customer visits, events, and conferences across the UK and Europe Company lunches twice a week Monthly socials & bi-annual team offsites \n

Responsibilities

Manage and optimize data annotation and machine learning workflows to ensure high-quality data for AI models. Act as a trusted advisor to clients while collaborating with product and engineering teams to improve AI training processes.