Digital Success Analytics Engineer

100 Salesforce, Inc.; Austin (TX); On-site; USD 90,000 - 120,000; Full time

Benefits offered by this job

Time off programs

Medical insurance

Dental insurance

Vision insurance

Mental health support

Parental leave

Life and disability insurance

401(k)

Employee stock purchase program

Job summary

Salesforce is seeking an experienced analyst to power digital success analytics. You’ll analyze customer behaviors, design decision-grade insights, and influence leadership decisions with actionable recommendations.

You’ll build predictive models and self-serve analytics while ensuring governance, privacy, and ethical AI use. Join a team shaping insights across Digital Success portfolios.

Qualifications

• Bachelor’s or Master’s degree in computer science, statistics, data science, AI, information technology, or related field.
• 7–8 years of analytics experience as Data Analyst, Analytics Engineer, Data Scientist, or BI Engineer.
• Experience in management consulting is a plus: structuring ambiguous problems and providing executive-ready recommendations.
• Advanced SQL (Snowflake) and Python for analysis, modeling, and automation.
• Strong statistics background: distributions, hypothesis testing, confidence intervals, and experiment design.
• Experience building predictive or ML models (regression, classification, clustering, time series, anomaly detection).
• Proficiency in Tableau and other BI tools.
• Familiarity with Salesforce ecosystem, Git and CI/CD.

Responsibilities

• Deliver decision-grade insights by analyzing customer, case, chat, voice, and in-app behavior.
• Turn findings into clear recommendations for leaders.
• Measure and compare product performance with launch-tracking, A/B, and cohorts.
• Define consistent KPIs and methodology for trusted results.
• Build predictive and statistical solutions from prototype to production.
• Define North Star metrics for the DS portfolio and ROI impact.
• Build dashboards and data stories for executives and stakeholders.

Skills

SQL

Python

Tableau

ML models

Statistics

Git

Data modeling

Consulting skills

Education

Bachelor’s or Master’s in CS/Statistics/Data Science/AI/IT

Tools

Snowflake

Git

Tableau

Job description

About Salesforce

Salesforce is the #1 AI CRM, where humans with agents drive customer success together. Here, ambition meets action. Tech meets trust. And innovation isn’t a buzzword — it’s a way of life. The world of work as we know it is changing and we're looking for Trailblazers who are passionate about bettering business and the world through AI, driving innovation, and keeping Salesforce's core values at the heart of it all. Ready to level‑up your career at the company leading workforce transformation in the agentic era? You’re in the right place! Agentforce is the future of AI, and you are the future of Salesforce.

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.

Unleash Your Potential

When you join Salesforce, you’ll be limitless in all areas of your life. Our benefits and resources support you to find balance and be your best, and our AI agents accelerate your impact so you can do your best. Together, we’ll bring the power of Agentforce to organizations of all sizes and deliver amazing experiences that customers love.

Accommodations

If you need a reasonable accommodation during the application or the recruiting process, please submit a request via this Accommodations Request Form. Please note that Salesforce uses artificial intelligence (AI) tools to help our recruiters assess and evaluate candidates’ resumes and qualifications throughout the recruiting process. Humans will always make any candidate selection and hiring decisions. Please see our Candidate Privacy Statement for more information about how we use your personal data and your rights, including with regard to use of AI tools and opt out options.

Posting Statement

Salesforce is an equal opportunity employer and maintains a policy of non‑discrimination with all employees and applicants for employment.

What does that mean exactly? It means that at Salesforce, we believe in equality for all. And we believe we can lead the path to equality in part by creating a workplace that’s inclusive, and free from discrimination.

Know your rights: workplace discrimination is illegal. Any employee or potential employee will be assessed on the basis of merit, competence and qualifications – without regard to race, religion, color, national origin, sex, sexual orientation, gender expression or identity, transgender status, age, disability, veteran or marital status, political viewpoint, or other classifications protected by law.

This policy applies to current and prospective employees, no matter where they are in their Salesforce employment journey. It also applies to recruiting, hiring, job assignment, compensation, promotion, benefits, training, assessment of job performance, discipline, termination, and everything in between.

Recruiting, hiring, and promotion decisions at Salesforce are fair and based on merit. The same goes for compensation, benefits, promotions, transfers, reduction in workforce, recall, training, and education.

In the United States, compensation offered will be determined by factors such as location, job level, job‑related knowledge, skills, and experience. Certain roles may be eligible for incentive compensation, equity, and benefits.

• time off programs
• medical
• dental
• vision
• mental health support
• paid parental leave
• life and disability insurance
• 401(k)
• an employee stock purchasing program

More details about company benefits can be found at the following link: https://www.salesforcebenefits.com.

We’re Salesforce, the Customer Company, inspiring the future of business with AI + Data + CRM. Leading with our core values, we help companies across every industry blaze new trails and connect with customers in a whole new way. And, we empower you to be a Trailblazer, too — driving your performance and career growth, charting new paths, and improving the state of the world. If you believe in business as the greatest platform for change and in companies doing well and doing good – you’ve come to the right place.