About Welo Data
Welo Data, a Welo Global brand, is the multilingual data and evaluation partner for foundation labs and enterprises deploying GenAI systems globally. They deliver the human judgment, data infrastructure, and evaluation systems that ensure AI models perform reliably across languages, cultures, and real-world contexts, at every stage from training through deployment. Its global network of 500,000+ vetted experts spans 300+ languages and locales, enabling high-quality multilingual data creation and structured model evaluation across the full spectrum of modern AI applications — from large language models and voice and speech systems to agentic workflows and robotics and embodied AI. This breadth of linguistic, cultural, and domain expertise enables Welo Data to address critical AI development challenges, including safety, bias, inclusivity, and cross-lingual reliability. A unified global operating model, led by specialized program and quality experts and grounded in assessment-driven talent selection, localized rubrics, and continuous calibration, ensures consistent performance across languages, domains, and modalities. Underpinning all of this is NIMO™ (Network Identity Management and Operations), Welo Data's proprietary identity and fraud-prevention framework. Built to maintain data integrity and workforce trust across a global contributor base, NIMO combines advanced verification, continuous monitoring, and structured QA to ensure every dataset is accurate, traceable, and culturally grounded. welodata.ai
Role Purpose:
Oversees multi-project portfolios for AI training data programs. Leads delivery and scale: capacity planning, vendor management, and process automation. Ensures quality is consistent across projects, drives performance improvements, and plans work jointly with the Sr. Quality Analyst. Oversees a portfolio of programs that deliver and scale Generative AI data operations, including capacity planning, vendor management, and process automation.
Key Responsibilities
- Portfolio delivery: Plan and run a group of projects (collection, labeling, evaluation) end-to-end; set priorities, milestones, and handoffs across time zones.
- Governance & reporting: Run cadence (status, risk, exec updates); align scope and budget with account and operations leads.
- Capacity planning: Forecast and secure rater/annotator capacity; balance shifts, throughput, and skills across vendors and internal teams.
- Quality consistency: Partner with the Sr. Quality Analyst on acceptance criteria, audits, and corrective actions; keep guidelines aligned across projects.
- Process automation & tooling: Identify manual steps; pilot automation with Ops Tech (templates, scripts, RPA, API-driven flows); scale what works.
- Vendor management: Set expectations, SLAs, and playbooks; review performance and drive improvements or changes as needed.
- Financial control: Build and track budgets, burn, and margins; manage change orders to protect financial outcomes.
- Risk, issue & change: Lead root-cause and action plans; escalate high-impact items with options and impact.
- Client and stakeholder management: Lead planning and QBR-style reviews; explain results, risks, and next steps in clear terms.
- Team enablement: Coach PMs and Coordinators on planning, QA, and tools; support onboarding and skills growth (no formal line management required).
- Compliance & security: Ensure data handling, privacy, and access controls are followed across all workstreams.
Skills
- Portfolio planning and governance across several projects at once.
- Clear communication with senior clients and internal leaders; runs planning and QBR-style reviews.
- Strong use of spreadsheets, PM/task boards, and basic BI; familiarity with SQL/ETL concepts is a plus.
- Sound judgment on scope, time, cost, and quality trade-offs.
- Negotiation and escalation to resolve risks, issues, and change.
- Coaching mindset; guides PMs and Coordinators.
- Comfortable working with global, distributed teams (intermediate to advanced English).
Additional Qualifications
- Near-native English with strong writing and editorial skills.
- Hands-on experience with generative AI tools (text, voice, or video).
- Background in QA testing, rubric design, or AI safety/ethics evaluation.
- Familiarity with data-annotation platforms and model-evaluation tools.
- Ability to interpret code, datasets, and system workflows at a conceptual level (no coding required).
- Able to work independently and manage workflows effectively in a remote environment.
- Multilingual ability beyond English.
Scope and Autonomy
- Owns outcomes for a portfolio of small/medium projects or a large multi-workstream program.
- Works independently; defines delivery approach and engages leaders for non-standard items.
- Joint planning and shared responsibility for quality and client outcomes with the Sr. Quality Analyst.
Education Requirements
- Bachelor’s degree or equivalent experience in business, operations, data/engineering, or similar experience.
- 2–3 years leading multi-project portfolios or large multi-workstream programs in AI data, content review, labeling/annotation, or related fields.
- Background in capacity planning, vendor coordination, and quality systems; experience piloting or scaling process automation is a plus.
