Data Science & Analytics Expert

Role Overview

Mercor is partnering with leading AI labs on Project Atlas — an initiative to build realistic enterprise environments that frontier AI agents are trained and evaluated in. We're seeking experienced data-science and analytics professionals from Fortune 500 technology, financial-services, retail, and healthcare enterprises (e.g., Google, Meta, Netflix, Capital One, Amex, Walmart Labs, Target, UnitedHealth Group) to recreate the digital workspaces they run every day and design the tasks that genuinely challenge state-of-the-art AI.

You'll bring your expertise in applied data science, analytics engineering, experimentation, or ML-product work to build a high-fidelity environment that mirrors the tools, files, and cross-functional workflows of a modern enterprise data organization — and then author tasks grounded in the programs you actually run today.

Key Responsibilities

• Build a realistic digital workspace centered on the Drive folders you use day-to-day — the design docs, experiment write-ups, stakeholder decks, SQL snippets, notebook exports, model cards, dashboards, and email threads that reflect how you actually organize your work — with some representation of the platforms that support it (e.g., Databricks / SAS Studio, Tableau / Power BI, Informatica PowerCenter / Talend)
• Design multi-step tasks grounded in your real workflows that require navigating multiple apps, files, and stakeholders in a way that meaningfully challenges frontier AI agents
• Collaborate with other data-science and analytics experts in your field to design the environment, shape task scope, and review each other's scenarios for realism and rigor
• Work asynchronously with research teams to refine task designs and evaluation criteria for data-science agent benchmarks
• Contribute to frontier AI research and benchmarking — the work you produce directly informs how leading labs train and evaluate the next generation of AI systems

Ideal Qualifications

• BS / MS / PhD in a quantitative discipline
• 3+ years of full-time experience at a Fortune 500 technology, financial-services, retail, or healthcare enterprise
• Background in one or more areas such as:
  • Applied data science (forecasting, causal inference, ML modeling)
  • Analytics engineering / data modeling
  • Experimentation / A-B testing platforms and methodology
  • Product analytics and business-insights work
  • ML engineering or decision-science / operations-research
• Day-to-day use of Databricks / SAS Studio, Tableau / Power BI, and Informatica PowerCenter / Talend
• Strong analytical thinking and writing — able to translate data-science workflows into structured task specs

Compensation Note

• Task Completion Pay: Competitive and based on task quality (~$1,250 – $1,750 per completed task, subject to change as the project evolves)
• Performance Bonus: Top performers receive a weekly bonus incentive on top of their per task rate!
• Hourly Opportunity: Top performers may be invited to transition to an hourly compensation model based on sustained quality and throughput.