Senior Decision Scientist

The Experience

Data Solutions is the force that propels Salesforce into the AI era, informing—through trusted data and artificial intelligence—the path forward to be smarter in every dimension. Delivering everything from financial forecasting and customer health to adoption insights and curated data, Data Solutions serves as the unbiased partner to every data trailblazer across Salesforce.

As part of this organization, Product & Success Analytics operates as the strategic product analytics engine, partnering directly with CSG leadership to drive high-stakes decision-making through rigorous metrics and insights.

Embedded directly within Success Analytics, this role serves as a strategic partner to product, engineering, and data leaders - co-building the foundational data architectures, key performance metrics, and AI-driven capabilities needed to propel product adoption.

The Senior Decision Scientist will serve as a lead technical authority on the team, leveraging advanced data science, statistical modeling, and quantitative analytics to power mission-critical operational systems.

In this role, you 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.

Working in tight partnership with Product Leadership, senior executive stakeholders, and cross-functional engineering teams, you will apply rigorous analytics to architect next-generation compensation and adoption metrics, drive automated data frameworks to scale M&A signal ingestion across incoming acquisitions, and enforce top-tier data trust and SLA reliability across underlying analytics pipelines.

What You'll Actually Be Doing

• Requirements & Data Discovery: Partner directly 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 across Sales & CSG prior to rollout.
• End-to-End Metric Build & Validation: Design, build, and statistically validate scoring logic and underlying data pipelines, ensuring calculations are robust, audit-ready, and resilient against edge cases.
• M&A Signal Integration & Scalable Data Frameworks: Serve as the lead Decision Scientist for designing and executing M&A data integration frameworks, transitioning signal ingestion from a manual "pull" model to an automated "push" model to drop onboarding cycle times from weeks to days.
• Strategic Advisory & Executive Alignment: Apply rigorous quantitative methods—including experimentation, causal inference, and tradeoff analysis—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, incorporating AI solutions into daily analytical workflows to accelerate time-to-insight.
• Data Integrity, Reliability & SLA Optimization: Own analytical health, automated anomaly detection, and root-cause data investigations for product adoption telemetry and underlying tables, dramatically reducing SLAs for bug resolution during critical compensation cycles.
• Technical Leadership & Mentorship: Elevate the team's technical bar by establishing best practices for statistical rigor and feature engineering while mentoring peers across Success Analytics and Data Solutions.

You're Our Person If...

• Proven Experience: 8+ years of hands-on experience in Data Science, Decision Science, Advanced Analytics, or Quantitative Product Analysis in enterprise SaaS environments, with a proven track record of deploying models that move business needles.
• Agentic AI & Systems Engineering: Demonstrated experience architecting agentic workflows, orchestrating LLMs, or building automated, self-correcting data science systems. You don't just use AI; you build systems that leverage it.
• Technical Mastery: High proficiency in statistical programming languages (Python or R) and modern data science stacks, along with exceptional SQL skills for complex feature engineering and database performance tuning.
• Quantitative Foundation: Master's degree or MBA in a highly quantitative discipline (e.g., Computer Science, Engineering, Economics, Statistics, or Applied Mathematics).
• Domain & Business Acumen: Proven track record applying data science to business metrics, health scoring algorithms, or incentive compensation structures across Sales and Customer Success organizations.
• Executive Presence: Exceptional communication skills with the ability to build trust with C-suite executives, align technical teams, and tell a compelling story with data.
• Entrepreneurial Execution: A "get-it-done" mindset with a strong bias for action, comfortable operating with high autonomy in a global, matrixed environment.