Engagement Manager - Marketing Data Science

Role: Engagement Manager - Marketing Data Science

Location: San Francisco, CA (Hybrid: 2-3 days from office/ week)

Compensation: $160k - $180k + 15% Bonus

Role and Responsibilities:

As the Marketing Data Science Engagement Manager, you will lead analytics that power growth across the customer lifecycle owning product analytics, email & lifecycle analytics, instrumentation, and forecasting. You will serve as the clients primary analytics partner, translating business goals into measurable experiments, building reliable data foundations, and scaling models that improve acquisition, activation, engagement, and retention.

• Serve as the main client contact, engaging with senior stakeholders across Product, Growth/Marketing, and Data.
• Design and optimize product & email analytics (funnels, cohorts, retention, content tests) to drive clear business outcomes.
• Define and implement tracking plans and event taxonomies; oversee instrumentation and data quality for accurate reporting.
• Build and refine forecasting models (timeseries, causal) for demand, traffic, signups, MAU, revenue, and channel volumes.
• Establish test and learn frameworks for campaigns and product features (A/B, incrementality, lift) with rigorous measurement.
• Create executive ready dashboards/KPIs for campaign performance, product health, and growth levers; recommend actions.
• Partner with engineering and MarTech teams to operationalize models and analytics (e.g., segmentation, triggers, journeys).
• Lead analytical projects with on/offshore teams; ensure quality, timeliness, and smooth productionization of solutions.

Candidate Profile:

• 7+ years of experience in Marketing Data Science, along with in consulting, solution design and client management
• Preferred experience in marketing analytics in banking industry in the following fields:
• Experience developing/expanding GTM strategies for new banking products
• Working across and a solid grasp of various business functions (Marketing, Product, Engineering, Analytics, and Finance)

• o Previous experience in the Financial/FinTech industry is a plus

• Technical proficiency in tools like SQL, Python
• Demonstrable leadership ability, superior problem solving and people management skills