Senior Decision Scientist

Salesforce•Seattle, WA

2d•$172,500 - $285,800

About The Position

The Senior Decision Scientist is part of Salesforce’s Product & Success Analytics organization within Data Solutions, serving as a strategic partner to product, engineering, and data leaders. The role is the team’s lead technical authority, applying advanced data science, statistical modeling, and quantitative analytics to mission-critical operational systems. It owns the modeling, data architecture, and statistical rigor behind product adoption telemetry used to govern performance metrics and compensation target-setting across Sales and Customer Success (CSG). The position partners with business stakeholders and senior executives to translate compensation strategies and operational goals into quantitative requirements, build and validate metrics and pipelines, and assess proposed changes through historical modeling and scenario analysis. It also leads scalable M&A signal integration, supports AI-driven analytical workflows and autonomous agents, and maintains data integrity, reliability, and service-level performance. The role provides technical leadership and mentorship across Success Analytics and Data Solutions.

Requirements

At least 8 years of hands-on experience in Data Science, Decision Science, Advanced Analytics, or Quantitative Product Analysis in enterprise SaaS environments.
A track record of deploying models that produce meaningful business impact.
Demonstrated experience architecting agentic workflows, orchestrating LLMs, or building automated, self-correcting data science systems.
High proficiency in Python or R and modern data science stacks.
Exceptional SQL skills, including complex feature engineering and database performance tuning.
A master's degree or MBA in a highly quantitative discipline such as Computer Science, Engineering, Economics, Statistics, or Applied Mathematics.
Experience applying data science to business metrics, health-scoring algorithms, or incentive compensation structures across Sales and Customer Success organizations.
Exceptional communication skills and the ability to build trust with C-suite executives, align technical teams, and communicate insights through data.
A bias for action and ability to operate autonomously in a global, matrixed environment.

Responsibilities

Partner with business stakeholders to translate compensation strategies and operational goals into quantitative requirements and data definitions.
Explore usage and telemetry data, conduct scenario modeling, and perform historical backfills to quantify the impact of proposed metric changes across Sales and Customer Success before rollout.
Design, build, and statistically validate scoring logic and data pipelines, ensuring calculations are robust, audit-ready, and resilient to edge cases.
Design and execute M&A data integration frameworks, moving signal ingestion from a manual pull model to an automated push model to reduce onboarding cycle times.
Use experimentation, causal inference, and tradeoff analysis to inform product initiatives, advise senior leaders, and translate complex insights into business strategies.
Architect, evaluate, and govern autonomous AI agents, incorporating AI solutions into analytical workflows.
Own analytical health, automated anomaly detection, and root-cause data investigations for product adoption telemetry and underlying tables.
Improve data reliability and reduce bug-resolution SLAs during critical compensation cycles.
Establish best practices for statistical rigor and feature engineering, and mentor peers across Success Analytics and Data Solutions.