Senior Agentic AI & Decision Scientist

Salesforce, Inc.; San Francisco (CA); On-site; USD 173,000 - 260,000; Full time

Job summary

Salesforce, Inc. is seeking a Senior Decision Scientist to lead advanced data science efforts for product adoption and compensation telemetry. You’ll own modeling, data architecture, and rigorous analytics across Salesforce’s Success Analytics and Data Solutions teams.

You will collaborate with product leadership and cross-functional engineers to build scalable data frameworks, drive experimentation, and govern AI lifecycle integration to accelerate insights and decisions.

Qualifications

• 8+ years of hands-on data science/decision science experience in enterprise SaaS.
• Experience architecting agentic workflows or LLM-enabled data systems.
• Strong proficiency in Python or R and SQL with a record of impactful modeling.

Responsibilities

• Partner with stakeholders to translate compensation strategies into quantitative requirements.
• Perform data exploration, modeling, and backfills to quantify metric changes.
• Design and validate scoring logic and data pipelines for robustness and auditability.
• Lead M&A data integration frameworks to speed onboarding and data signal ingestion.
• Advise executives with quantitative methods to inform product initiatives.
• Architect and govern autonomous AI agents to automate data science tasks.
• Ensure data integrity, anomaly detection, and SLA reliability across analytics pipelines.
• Mentor peers to raise statistical rigor and feature engineering standards.

Skills

Python

R

SQL

Statistical modeling

Experimentation

Communications

Education

Master's degree or MBA in a quantitative discipline

Tools

Python

R

SQL

Job description

Salesforce, Inc. is seeking a Senior Decision Scientist to lead advanced data science efforts for product adoption and compensation telemetry. You’ll own modeling, data architecture, and rigorous analytics across Salesforce’s Success Analytics and Data Solutions teams.

You will collaborate with product leadership and cross-functional engineers to build scalable data frameworks, drive experimentation, and govern AI lifecycle integration to accelerate insights and decisions.