Senior Project Manager - Generative AI Data Operations

• 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 P1 PMs and C2–C3 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. 
   

• People Management: Gather feedback, provide training and coaching, foster team cohesion through team-building activities, conduct performance reviews, and support staffing and retention efforts. Additionally, collaborate with PST, Finance and recruiters for onboarding, offboarding, and addressing employee needs, while maintaining clear communication and updated SOPs to guide team.

• Collaboration with Recruitment: Work closely with the recruiting team to manage hiring efforts by communicating requirements (e.g., language needs), upcoming projects, and candidate statuses. Review, evaluate, and select qualified candidates from recruiters and decide on next steps (e.g., assessments, interviews, or client presentation).

. Bachelor’s degree or equivalent experience in business, operations, data/engineering, or similar.

• 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.

• 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 P1 PMs and C2–C3 Coordinators 

• Comfortable working with global, distributed teams (intermediate to advanced English).

• 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.

• 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.