Strategic Projects Lead

About the role

The day-to-day is intense by design. You'll juggle multiple programs at once, each with its own contributors, quality standards, and customer expectations. You'll make hard calls with incomplete information, hold contributors and vendors accountable, and find creative solutions to problems that don't have obvious answers. The expectation is simple: own the outcome, no excuses. High performers here grow faster than anywhere else in the industry. The bar is high — and that's the point.

Responsibilities

• Own end-to-end execution of multi-million-dollar AI data programs, directly accountable for data quality, throughput, margins, and customer satisfaction
• Design and manage data pipelines from customer specifications through final delivery, including taxonomy design, quality frameworks, and incentive structures
• Manage and performance-manage 1,000+ distributed contributors — sourcing, vetting, training, and coaching domain experts at scale
• Serve as the primary client contact for AI lab researchers: deliver consistent reporting, anticipate needs, and identify opportunities for program expansion
• Run daily internal war rooms to stay ahead of quality issues, bottlenecks, and delivery risks
• Partner cross-functionally with Engineering, Product, and Growth to build and improve operational infrastructure
• Deploy AI automations and data-driven tools to improve output quality and pipeline efficiency

Requirements

• 2–4+ years in consulting, finance, startups, or operationally intensive roles
• Strong analytical skills; proficiency in SQL or Python preferred
• Track record managing complex, cross-functional projects with measurable outcomes
• Entrepreneurial mindset; comfortable building from scratch in ambiguous environments
• Exceptional communication skills and client-facing presence

Preferred Qualifications

• Technical or STEM degree (CS, Data Science, Engineering, or related field)
• Experience managing distributed workforces or marketplace operations
• Exposure to AI data, RLHF, or model evaluation workflows
• Background in investment banking, private equity, or management consulting