Digital Success Analytics Engineer

What you'll do

• Analyze customer behavior data to uncover drivers for resolution, adoption, CSAT, and attrition.
• Design and execute A/B tests, cohort analyses, and launch-tracking to measure product performance.
• Develop and deploy predictive models for forecasting, segmentation, anomaly detection, and root-cause analysis.
• Establish canonical metric definitions and KPIs to ensure a single source of truth across functions.
• Create Tableau dashboards and data stories to communicate actionable insights to executive leadership.
• Build frameworks and semantic definitions to enable self-serve and AI-augmented analytics for users.
• Define data instrumentation requirements and tracking specifications with product and engineering partners.
• Apply privacy standards and ensure the ethical use of AI/ML models in all analyses.

What we're looking for

• 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.
• Advanced SQL (Snowflake) and Python skills for analysis, modeling, and automation.
• Strong statistical foundation including probability distributions, hypothesis testing, confidence intervals, p-values, and experiment design.
• Experience building predictive or ML models such as regression, classification, clustering, time series, and anomaly detection.
• Proficiency in Tableau and other BI tools.
• Familiarity with the Salesforce ecosystem.
• Experience at a consulting firm or in an internal strategy/consulting function (preferred).