Vice President, Analytics Lead - Mid-Market Payments Marketing

As a Vice President, Analytics Lead in the Mid-Market Payments Marketing team, you will drive a data-driven decision-making culture by delivering complex analytical projects that power marketing and sales outcomes. Translate data into clear, actionable narratives that deepen understanding of our customers, journeys, products, and market. Partner closely with technology, data, marketing, sales, finance, and operational analytics to align work to business priorities and deliver lement scalable measurement frameworks for performance, channel attribution, and automated insight generation.

Champion best practices in analytics, data governance, and measurement to foster continuous learning and innovation.

Manage a pragmatic pipeline of analytical initiatives tied to mid-market B2B marketing objectives, ensuring timely, high-quality delivery. Proactively evaluate and integrate new data sources and capabilities to keep our analytics toolkit current. Design and run structured tests that validate data value and inform scale decisions. Cultivate effective vendor partnerships to accelerate innovation across enrichment, automation, and AI. Set standards for reusable assets—dashboards, models, and analyses—that improve repeatability and time-to-insight.

• Lead end-to-end delivery of analytical solutions from scoping and design through development, QA, deployment, and enablement.
• Build trusted partnerships across technology, data engineering/warehousing, marketing, sales, finance, and analytics to ensure alignment and execution.
• Prioritize and manage a transparent pipeline of analytics projects that support marketing strategy and commercial goals.
• Translate complex findings into clear stories, recommendations, and artifacts that drive decision‑making by non‑technical stakeholders.
• Identify, evaluate, and manage vendors to introduce new capabilities in data enrichment, automation, and artificial intelligence.
• Proactively source and acquire new data; design and execute test‑and‑learn plans to assess signal quality, lift, and ROI.
• Develop scalable measurement and reporting frameworks for performance tracking, channel attribution, and automated recurring analysis.
• Establish and promote best practices in analytics methods, documentation, code hygiene, and data governance.
• Partner with marketing and sales to embed insights into planning, targeting, messaging, and funnel optimization.
• Create reusable analytical assets (dashboards, templates, feature stores) to accelerate delivery and consistency.
• Present progress, risks, and outcomes to stakeholders and senior leadership, ensuring clarity on business impact.

• Minimum 10 years of experience in analytics, data science, or business intelligence delivering business‑impacting work.
• Proven experience designing and building dashboards and conducting exploratory analysis to inform strategy.
• Hands‑on proficiency with Python or R and statistical analysis; strong SQL and data‑wrangling skills.
• Demonstrated ability to develop and operationalize AI and machine learning models for marketing or sales use cases.
• Track record managing complex, cross‑functional projects end‑to‑end with clear timelines and outcomes.
• Excellent communication skills; ability to present technical concepts to non‑technical audiences and drive adoption.
• Experience collaborating across data, technology, marketing, and sales teams to deliver aligned outcomes.
• Proficiency with data visualization tools (e.g., Tableau or Power BI) and version control/workflow best practices.
• Bachelor’s degree in a quantitative field such as Statistics, Mathematics, Computer Science, Economics, or related discipline.

• Experience in payments, financial services, or B2B marketing analytics.
• Expertise with marketing measurement and experimentation, including attribution frameworks (e.g., multi‑touch attribution) and media mix modeling.
• Experience evaluating and managing third‑party data/AI vendors and contracts.
• Familiarity with data governance, privacy‑by‑design, and compliant data usage in marketing contexts.
• Master’s degree in a quantitative field.
• Experience with cloud data platforms and analytics services (e.g., AWS, GCP, or Azure) and MLOps practices.
• Experience building scalable, reusable analytics assets and enabling self‑service for business users.