Senior Decision Scientist

Salesforce is seeking a Senior Decision Scientist to join the Data Solutions team. The successful candidate will serve as a lead technical authority, leveraging advanced data science, statistical modeling, and quantitative analytics to power mission-critical operational systems.

The Senior Decision Scientist will own the modeling, data architecture, and statistical rigor behind product Adoption telemetry - systems that directly govern performance metrics and compensation target-setting across the organization for Sales & CSG.

Key responsibilities include:

• Requirements & Data Discovery: Partner with business stakeholders to translate complex compensation strategies and operational goals into precise quantitative requirements and data definitions.
• Impact Analysis & Historical Modeling: Perform deep-dive data exploration, scenario modeling, and historical backfills on underlying usage and telemetry tables to quantify the impact of proposed metric changes.
• End-to-End Metric Build & Validation: Design, build, and statistically validate scoring logic and underlying data pipelines.
• M&A Signal Integration & Scalable Data Frameworks: Design and execute M&A data integration frameworks, transitioning signal ingestion from a manual "pull" model to an automated "push" model.
• Strategic Advisory & Executive Alignment: Apply rigorous quantitative methods to inform product initiatives, advise senior leadership, and translate complex insights into clear business strategies.
• Agentic Systems & AI Lifecycle: Architect, evaluate, and govern autonomous AI agents to automate complex data science tasks.
• Data Integrity, Reliability & SLA Optimization: Own analytical health, automated anomaly detection, and root-cause data investigations for product adoption telemetry and underlying tables.
• Technical Leadership & Mentorship: Establish best practices for statistical rigor and feature engineering while mentoring peers.

The ideal candidate will have:

• 8+ years of hands-on experience in Data Science, Decision Science, Advanced Analytics, or Quantitative Product Analysis in enterprise SaaS environments.
• Demonstrated experience architecting agentic workflows, orchestrating LLMs, or building automated, self-correcting data science systems.
• High proficiency in statistical programming languages (Python or R) and modern data science stacks, along with exceptional SQL skills.
• Master's degree or MBA in a highly quantitative discipline.
• Proven track record applying data science to business metrics, health scoring algorithms, or incentive compensation structures.
• Exceptional communication skills with the ability to build trust with C-suite executives.

The typical base salary range for this position is $172,500 - $260,100 annually. In select cities within the San Francisco and New York City metropolitan area, the base salary range for this role is $207,800 - $285,800 annually.