About the RoleOur dataset is valuable only to the extent that it is authentic, complete, and operationally usable across all machine learning and operational workflows. We maintain and continuously evaluate data quality through a rigorous hybrid approach combining models with verification.
In this role, you will build, scale, and lead our global review operations organization, taking full operational accountability for critical quality and performance metrics including review coverage, multi-pass accuracy, reviewer throughput, SLA compliance, and unit economics/cost management. You will establish robust, continuous feedback loops connecting local merchants, external BPO partners, field capture teams, and executive management to drive dataset standards. Serving as the operational partner to our quality engineering and machine learning teams, you will ensure our operational insights directly inform, calibrate, and improve automated models, annotation workflows, and field capture guidelines.
What You Will Be Doing:- Team leadership: Lead, structure, and scale a high-volume review and approval organization encompassing internal teams and vendor partners. Hire, develop, and coach operational team leads, establish clear performance management frameworks, and manage headcount, shift scheduling, and operational budgets to optimize unit economics.
- Coverage and accuracy: Take end-to-end ownership of review coverage, SLA adherence, reviewer throughput, and multi-tier accuracy. Design and execute regular calibration sessions, inter-annotator agreement audits, and QA reporting to enforce a unified quality standard across all global locations.
- Merchant quality: Define and enforce merchant approval workflows, automated pre-payout verification checks, and merchant compliance standards. Publish weekly quality scorecards and establish structured feedback loops with sales, account management, and business development teams with strict service level agreements for resolution.
- Engineering partnership: Partner closely with data engineering, product, and AI research teams to embed our evaluation insights into model training. Define active learning edge cases, continuously refine automated rule checks, and report operational quality metrics and cost models to executive leadership on a weekly cadence.
- Integrity: Lead the operational protocol and response framework when fraud, data tampering, or platform integrity issues are detected. Collaborate with engineering, legal, and finance to investigate root causes, mitigate risk, enforce merchant suspensions, and prevent future recurring vulnerabilities.
Who you are:- Experience: 5 to 8+ years of progressive operational leadership experience managing large-scale review, merchant, trust and safety, community, or data quality operations in high-growth environments.
- Quality programmes: Demonstrated track record of architecting comprehensive quality control frameworks from scratch, including balanced scorecards, statistical sampling techniques, cross-team calibration sessions, and targeted coaching loops.
- Analytical skills: Exceptional analytical acumen with proficiency in building operational dashboards, writing SQL queries, modeling unit economics, and communicating complex statistical performance data effectively with AI/ML engineers.
Even Better If You Have:- Industry: Direct experience in merchant operations, content moderation, or onboarding at a top-tier marketplace, hyper-local logistics, or delivery platform operating across Southeast Asia or international markets.
- Vendors: Hands-on experience procuring, negotiating, and managing third-party BPO vendor relationships, setting up vendor SLAs, and running quarterly business reviews
- Data quality: Deep familiarity with computer vision data collection, video stream evaluation, bounding-box/semantic segmentation annotation pipelines, or synthetic data training setups.
A Note on ApplyingStudies show women and candidates from underrepresented groups often only apply when they meet 100% of the listed qualifications, while others apply after meeting 60%. If you don't check every box above but believe you can do the job, we encourage you to apply - we're looking for capability and trajectory, not a perfect checklist match.
