Description
We are seeking a Decision Scientist Lead - Cloud Analytics to join our team at Salesforce. As a Decision Scientist Lead, you will apply rigorous quantitative and statistical methods to inform product initiatives and diagnose complex business challenges. You will serve as a trusted advisor and thought leader to senior leadership, translating complex analytical insights into clear, high-impact business strategies.
Responsibilities:
- Strategic Advisory: Apply rigorous quantitative and statistical methods, including experimentation, causal inference, tradeoff analysis, and modeling under uncertainty to inform product initiatives and diagnose complex business challenges.
- Agentic Systems & AI Lifecycle: 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.
- Stakeholder Partnership: 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.
- Roadmap Ownership: Collaborate with cross-functional leaders to define the AgentExchange product analytics roadmap, prioritizing initiatives that drive maximum value and scale.
- Metrics & Growth: Define and track the critical product metrics required to accelerate AgentExchange adoption and long-term customer value.
- Technical Leadership & Mentorship: Elevate the team's technical bar by establishing best practices for statistical rigor and agentic engineering, while mentoring peers across the broader Cloud Analytics organization.
Requirements:
- Proven Experience: 8+ years of hands-on experience in data science, decision science, or advanced analytics, with a proven track record of deploying analytical models that move business needles.
- Agentic AI & Systems Engineering: Demonstrated experience architecting agentic workflows, orchestrating LLMs, or building automated, self-correcting data science systems.
- Technical Mastery: High proficiency in statistical programming languages (Python or R) and modern data science stacks, along with exceptional SQL skills for complex feature engineering and database performance tuning.
- Quantitative Foundation: Master’s degree or MBA in a highly quantitative discipline (e.g., Computer Science, Engineering, Economics, Statistics, or Applied Mathematics).
- Executive Presence: Exceptional communication skills with the ability to build trust with C-suite executives, align technical teams, and tell a compelling story with data.
- Entrepreneurial Execution: A "get-it-done" mindset with a strong bias for action, comfortable operating with high autonomy in a global, matrixed environment.