Decision Scientist Lead - Cloud Analytics

Salesforce Salesforce · Enterprise · San Francisco, CA +1

This role focuses on architecting, evaluating, and governing autonomous AI agents to automate data science tasks and scale analytics capabilities. The Decision Scientist Lead will partner with product and engineering teams to define the product analytics roadmap, define key metrics, and lead technical mentorship in agentic engineering and statistical rigor. The role requires significant experience in data science, agentic AI systems engineering, and proficiency in programming languages like Python or R.

What you'd actually do

  1. Architect, evaluate and govern autonomous AI agents to automate complex data science tasks, scaling our capability to deliver descriptive and prescriptive analytics at enterprise speed.
  2. Incorporate AI solutions into daily analytical workflows to drive faster time-to-insight.
  3. Partner with cross-functional squads of analysts and engineers to diagnose complex business problems—applying rigorous quantitative methods and business context to provide clear descriptive analysis and actionable prescriptive solutions.
  4. Collaborate with cross-functional leaders to define the AgentExchange product analytics roadmap, prioritizing initiatives that drive maximum value and scale.
  5. Define and track the critical product metrics required to accelerate AgentExchange adoption and long-term customer value.

Skills

Required

  • 8+ years of hands-on experience in data science, decision science, or advanced analytics
  • Demonstrated experience architecting agentic workflows, orchestrating LLMs, or building automated, self-correcting data science systems
  • High proficiency in statistical programming languages (Python or R)
  • Exceptional SQL skills
  • Master’s degree or MBA in a highly quantitative discipline (e.g., Computer Science, Engineering, Economics, Statistics, or Applied Mathematics)
  • Exceptional communication skills
  • A "get-it-done" mindset with a strong bias for action

What the JD emphasized

  • proven track record of deploying analytical models that move business needles
  • Demonstrated experience architecting agentic workflows, orchestrating LLMs, or building automated, self-correcting data science systems
  • You don't just use AI; you build systems that leverage it

Other signals

  • architecting agentic workflows
  • orchestrating LLMs
  • building automated, self-correcting data science systems
  • incorporate AI solutions into daily analytical workflows