Senior Project Manager - Generative AI Data Operations

Role Purpose:

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.