Senior Decision Scientist

Salesforce•San Francisco, CA

About The Position

Salesforce’s Experience Data Solutions organization provides trusted data and AI insights across the company. Product & Success Analytics partners with CSG leadership on strategic product analytics and high-stakes decisions. Embedded in Success Analytics, the Senior Decision Scientist partners with product, engineering, data, and executive leaders to build data architectures, performance metrics, and AI-driven capabilities that support product adoption. The role is the team’s lead technical authority for modeling, data architecture, and statistical rigor behind product adoption telemetry. These systems inform organizational performance metrics and compensation target-setting across Sales and CSG. The Senior Decision Scientist also leads scalable M&A signal integration, strengthens data trust and pipeline reliability, and develops AI-enabled analytical workflows.

Requirements

At least 8 years of hands-on experience in Data Science, Decision Science, Advanced Analytics, or Quantitative Product Analysis in enterprise SaaS environments.
Proven 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.
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, including the ability to build trust with C-suite executives, align technical teams, and communicate a compelling data narrative.
Ability to execute with autonomy and a strong bias for action in a global, matrixed environment.

Responsibilities

Translate compensation strategies and operational goals into quantitative requirements and data definitions with business stakeholders.
Explore usage and telemetry data, conduct scenario modeling and historical backfills, and quantify the impact of proposed metric changes across Sales and CSG.
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 that automate signal ingestion and reduce onboarding cycle times.
Use experimentation, causal inference, and tradeoff analysis to inform product initiatives, advise senior leaders, and translate insights into business strategies.
Architect, evaluate, and govern autonomous AI agents and incorporate AI solutions into analytical workflows.
Own analytical health, automated anomaly detection, and root-cause investigations for product adoption telemetry and underlying tables; improve bug-resolution SLAs.
Establish statistical rigor and feature-engineering best practices and mentor peers across Success Analytics and Data Solutions.