Data Scientist, Claude Code

Anthropic Anthropic · AI Frontier · Data Science & Analytics

Data Scientist role focused on analyzing developer interactions with Claude Code, a command-line AI coding assistant. The role involves defining metrics, designing experiments, and identifying insights to improve the product and developer experience, aiming to scale this AI-assisted development tool.

What you'd actually do

  1. Deep dive into how developers use Claude Code across different programming languages, project types, and workflows to provide insights that inform product strategy and feature development
  2. Design and implement metrics to quantify how Claude Code affects developer productivity, code quality, and development velocity across different use cases and skill levels
  3. Analyze patterns in human-AI collaboration within coding workflows, identifying opportunities to improve the handoff between developers and Claude for more effective task delegation
  4. Develop hypotheses about product changes, design controlled experiments with developer users, and analyze results to guide feature prioritization and development
  5. Identify friction points in the Claude Code user journey and provide data-driven recommendations to improve onboarding, retention, and long-term engagement

Skills

Required

  • 5+ years of experience in data science or analytics roles
  • Python
  • SQL
  • data visualization tools
  • command line environments
  • measuring productivity, engagement, and satisfaction metrics for technical users or B2B products
  • turning complex technical usage patterns into clear, actionable insights
  • exceptional written communication skills

Nice to have

  • Experience with software development workflows, version control systems, and common developer tools
  • Background in developer experience research or working with technical communities
  • Familiarity with AI/ML model outputs
  • experience analyzing human-AI interaction patterns
  • Experience with command line tools, terminal-based workflows
  • Understanding of software development lifecycle metrics and engineering productivity frameworks
  • Experience working at developer-focused companies or on products targeting software engineers
  • Knowledge of programming languages and ability to understand code-related usage patterns

What the JD emphasized

  • significant focus on product analytics
  • developer tools
  • AI-assisted development
  • technical products used by software engineers
  • measuring productivity, engagement, and satisfaction metrics for technical users or B2B products
  • AI/ML model outputs
  • human-AI interaction patterns
  • developer productivity measurement

Other signals

  • AI-powered developer tools
  • human-AI collaboration
  • developer productivity
  • AI-assisted development