Principal Decision Scientist
Listed on 2026-06-17
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IT/Tech
AI Engineer (Applied/Software), Data Scientist, Machine Learning/ ML Engineer
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.
CoreResponsibilities
- 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. Distinguish incremental gains from organic trends using data science and modeling techniques. - 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 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 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 similar. - Experience:
10+ years in a quantitative role with a proven track record deploying causal models or experimental frameworks in production. - Programming:
Adept proficiency in Python or R (PyData stack: Pandas, Num Py, Sci Py, Stats models, scikit‑learn). - Data Retrieval:
Mastery of SQL for complex extraction, feature engineering, and performance tuning in cloud data warehouses (e.g., Snowflake, Big Query). - Modeling Mastery:
Deep experience with high‑dimensional regression, time‑series analysis, and forecasting techniques. - Agentic Systems:
Familiarity with LLM evaluation metrics (LLM‑as‑a‑judge) and challenges of non‑deterministic AI outputs.
Accommodations:
If you need a reasonable accommodation during the application or recruiting process, please submit a request.
Posting Statement:
Salesforce is an equal‑opportunity employer. All employees and applicants will be assessed on merit, competence, and qualifications—without regard to protected characteristics. Recruiting, hiring, and promotion decisions are fair and based on merit.
Compensation:
In the United States, base salary ranges from $197,300 – $313,700 annually (or $237,700 – $344,700 in certain metro areas). Compensation may also include incentive, equity, and benefits.
Benefits:
Time off, medical, dental, vision, mental health support, paid parental leave, life and disability insurance, 401(k), employee stock purchasing program.
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