Digital Success Analytics Engineer

About the Team

The Digital Success Analytics team powers decisions for Digital customer experience success at Salesforce. We are becoming a truly data-driven enterprise. That means trusted, governed metrics, world-class self-serve analytics, and AI-ready insights that Customer Success teams use every day.

We own the vision, strategy, and execution of our internal Analytics and BI portfolio for Digital Success. Our analytics help teams improve the digital success experience, drive product adoption and customer satisfaction, and reduce attrition.

Our portfolio includes dashboards and scorecards, canonical metric definitions, experimentation and measurement frameworks, predictive and ML models, and self-serve, AI-assisted analytics. We partner with IT Data Engineering and Platform teams, who own the underlying pipelines and infrastructure. We act as an internal consulting partner to CS leaders, taking complex questions through to measurable business impact.

Roles and Responsibilities

• Deliver decision-grade insights: Analyze customer, case, chat, voice, and in-app behavior to uncover what drives resolution, deflection, adoption, CSAT, and attrition. Turn findings into clear recommendations for leaders.
• Measure and compare product performance: Design and run launch-tracking, A/B and cohort analyses, and comparative assessments (for example, AI agent vs. legacy experiences). Define consistent KPIs and methodology so results are trusted and comparable.
• Build predictive and statistical solutions: Develop models for forecasting, segmentation, propensity, anomaly detection, and root-cause analysis. Take them from prototype to production use.
• Define metrics and the “North Star”: Help define the North Star and success metrics for the DS portfolio. Move beyond vanity metrics to the signals that correlate with long-term growth. Support target setting and show impact and ROI.
• Champion a single source of truth for metrics: Establish canonical metric definitions and a common language across all customer success fuctions such as - Digital & Assisted support, Training, Certifications, Success Guides, and Architects. Work with Data Engineering to define requirements and validate golden datasets.
• Build dashboards and data stories: Create Tableau dashboards, scorecards, and visualizations that turn analysis into narrative for stakeholders up to executive level.
• Enable self-serve and AI-augmented analytics: Build frameworks, semantic definitions, and enablement (Tableau, MCP, and LLM-based tools) so users can explore trusted data with minimal friction.
• Data instrumentation and measurement strategy: Define what data must be captured to measure product success. Specify tracking requirements with product and engineering partners.
• Data governance and responsible AI: Apply privacy standards (GDPR/CCPA) in analysis and ensure ethical use of AI/ML models.
• Partner and influence: Work with business stakeholders to prioritize high-value analytics opportunities. Collaborate with IT Data Engineering, Architecture, Platform, and Governance, and with Analytics & Data Science partners.
• Operate with rigor: Work in agile delivery with clear documentation, and share best practices through storytelling and enablement.

Qualifications

Education & Experience

• Bachelor’s or Master’s degree in Computer Science, Statistics, Data Science, AI, Information Technology, or a related field.
• 7-8 years of experience in analytics, as a Data Analyst, Analytics Engineer, Data Scientist, or BI Engineer.
• Management consulting experience (a plus): Experience at a consulting firm, or in an internal strategy or consulting function, structuring ambiguous problems and turning findings into executive-ready recommendations.

Technical Expertise

• Advanced SQL (Snowflake) and Python for analysis, modeling, and automation.
• Strong statistical foundation, including probability distributions, hypothesis testing, confidence intervals, p-values, and experiment design. You can tell a genuine trend from statistical noise.
• Experience building predictive or ML models (regression, classification, clustering, time series, anomaly detection).
• Proficiency in Tableau and other BI tools.
• Familiarity with the Salesforce ecosystem.
• Git and basic CI/CD for reproducible, production-ready analytics.
• Working knowledge of data modeling concepts (pipelines, medallion architecture). Deep pipeline engineering is not required.

Analytical Skills

• Find insights and build predictive tools and visualizations for storytelling.
• Apply a consulting-style approach: structure ambiguous problems, run hypothesis-driven analyses, prioritize by impact, and present clear “so what” recommendations.
• Translate complex AI and data science research into practical, actionable solutions.
• Think strategically about emerging technologies.
• Communicate findings clearly to technical and executive audiences.

The “Modern” Analyst (AI Literacy)

• Augmented analytics: Use AI tools (Claude Code, Gemini, etc.) to automate cleaning and spot anomalies in large datasets.
• Prompt engineering for data: Use LLMs to write and debug complex SQL and Python.
• Agentic AI skills: Build agentic workflows and skills that automate reporting, such as scorecard refreshes and self-serve querying.

Nice to Have

• Master’s or PhD in Statistics, Data Science, Information Systems, Computer Science, or a related field. An MBA is also welcome.
• Management consulting experience in strategy, analytics, or transformation.
• Experimentation and causal inference experience.
• Familiarity with metadata management, data catalogs, and data discoverability.
• AI/ML product development in an enterprise context.