Principal Decision Scientist

Job Category

Data

Salesforce is the #1 AI CRM, where humans with agents drive customer success together. Here, ambition meets action. Tech meets trust. And innovation isn’t a buzzword — it’s a way of life. The world of work as we know it is changing and we’re looking for Trailblazers who are passionate about bettering business and the world through AI, driving innovation, and keeping Salesforce’s core values at the heart of it all.

As a Decision Scientist within the Data & Analytics organization, you will collaborate with cross‑functional teams to measure agentic impact, influence leaders with in-depth analysis, and strategically partner with Product Managers to shape the future of our work. You will work in a dynamic organization that sits at the intersection of analytics, AI, and automation, and is building the framework for the agentic enterprise in real time.

• Agentic Evaluation Frameworks:
  Develop and scale methodologies to evaluate the performance, reasoning, and reliability of agentic workflows and AI systems.
• Causal Inference & Attribution:
  Build sophisticated models to solve complex attribution problems. You will distinguish between incremental gains and organic trends using various data science and modeling techniques at your disposal.
• Experimental Design:
  Lead the design and analysis of complex experiments, moving beyond simple A/B testing into multivariate testing, switchback experiments, and quasi-experimental designs for cases where randomization is impossible.
• Statistical Leadership:
  Refine and author methodologies, frameworks, analytical packages, and mentor junior team members.
• Strategic Influence:
  Translate complex statistical findings into clear, actionable narratives for executive leadership, influencing the long-term product and business roadmap.

• Background:
  Master’s or PhD in a highly quantitative field such as Statistics, Mathematics, Economics, Computer Science, Operations Research, or a similar field.
• Experience:
  
  10+ years of experience in a quantitative role, with a proven track record of deploying causal models or experimental frameworks in a production environment.
• Programming:
  Adept proficiency in Python or R (specifically the PyData stack: Pandas, Num Py, Sci Py, Stats models, Scikit-learn).
• Data Retrieval:
  Mastery of SQL for complex data extraction, feature engineering, and performance tuning within cloud data warehouses (e.g., Snowflake, Big Query).
• Modeling Mastery:
  Deep experience in high-dimensional regression, time-series analysis, and forecasting techniques.
• Agentic Systems:
  Familiarity with LLM evaluation metrics (LLM-as-a-judge) and the unique statistical challenges posed by non-deterministic AI outputs.

If you need a reasonable accommodation during the application or the recruiting process, please submit a request via this Accommodations Request Form.

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