S

Salesforce

Builds cloud software for customer relationships

Senior Decision Scientist

Data ScienceConfirmed live in the last 24 hours
Full-timeIn-person

About the Job

Requirements

  • At least 8 years of hands-on experience in Data Science, Decision Science, Advanced Analytics, or Quantitative Product Analysis in enterprise SaaS environments, including a track record of deploying models that move business outcomes.
  • Experience architecting agentic workflows, orchestrating large language models, or building automated, self-correcting data science systems.
  • High proficiency in statistical programming languages such as Python or R, modern data science stacks, and SQL for 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.
  • Ability to communicate with C-suite executives, align technical teams, and present insights using data.
  • Ability to operate with high autonomy in a global, matrixed environment.

Responsibilities

  • Translate complex compensation strategies and operational goals into quantitative requirements and data definitions in partnership with business stakeholders.
  • Conduct data exploration, scenario modeling, and historical backfills on usage and telemetry tables to quantify 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 from weeks to days.
  • Use experimentation, causal inference, and tradeoff analysis to inform product initiatives, advise senior leadership, and translate insights into business strategies.
  • Architect, evaluate, and govern autonomous AI agents that automate data science tasks and incorporate AI solutions into analytical workflows.
  • Own analytical health, automated anomaly detection, and root-cause investigations for product adoption telemetry and underlying tables, reducing bug-resolution SLAs during compensation cycles.
  • Establish best practices for statistical rigor and feature engineering, and mentor peers across Success Analytics and Data Solutions.

About the Company

Salesforce

Builds cloud software for customer relationships

Salesforce sells cloud software that helps businesses manage sales leads, customer service, marketing, and online commerce from shared customer records. Its flagship Sales Cloud tracks accounts and deals, while Service Cloud organizes support cases and Agentforce builds AI assistants that act on company data. Teams access these tools through a browser and connect outside apps and data sources, with Slack for work messages and Tableau for visual analysis. Salesforce earns most of its revenue from software subscriptions and support, with professional services for implementation and training. Marc Benioff co-founded Salesforce in 1999, and its headquarters are in San Francisco, California.

Company size
10,001+
Company stage
IPO
Headquarters
San Francisco, CA
Founded
1999

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