Director of Marketing Analytics

At FNBO, our employees are the heart of our story-and we're committed to their success! Please see below the details of this career opportunity and how it fits into our organization's success.

The Director of Marketing Analytics within the Partnership Data & Modeling (PDM) division and serves as the analytical point of contact for the Partnership Marketing team. This role is responsible for translating the data assets, reports, and models developed across PDM into actionable marketing strategies that drive measurable business outcomes - including lower cost per acquisition, portfolio growth, effective targeting, and improved marketing performance across direct mail, acquisition, and account management programs.

• Translate data assets, predictive models, and BI reports developed across PDM into practical, actionable marketing strategies and recommendations.
• Proactively bring analytical insights and opportunities to the Partnership Marketing team - identifying where data can improve targeting, reduce waste, or unlock growth.
• Represent marketing analytics needs back to PDM - ensuring that model development, data portfolio, and BI priorities reflect the requirements of the marketing function.
• Maintain a clear understanding of Partnership Marketing's goals, campaigns, and challenges to ensure analytical support is timely, relevant, and impactful.

• Lead analytical support for campaign planning across direct mail (DM), acquisition, and account management programs - defining target audiences, channel mix, offer structures, and success metrics.
• Leverage PDM data assets and models to size audiences, forecast response rates, and optimize business outcomes for planned campaigns.
• Collaborate with PDM, Marketing and Marketing Engineering team to develop campaign briefs grounded in data, ensuring targeting strategies are aligned with acquisition, engagement, and retention objectives.

• Drive targeting precision across DM, acquisition, and account management by leveraging propensity models, segmentation frameworks, and data assets developed within PDM and/or external vendors.
• Translate model outputs into practical targeting strategies - defining selection criteria, suppression logic, and audience prioritization for each campaign.
• Continuously optimize targeting strategies based on campaign performance data, test results, and evolving business objectives - with a clear focus on reducing CPA and improving business outcomes.
• Implement champion/challenger targeting approaches to systematically test and improve selection strategies over time.
• Partner with PDM modeling teams to provide feedback on model performance in a marketing context, informing model recalibration and future development priorities.
• Identify opportunities to expand or refine targeting capabilities by surfacing gaps in current models or data assets to PDM leadership.

• Apply customer lifecycle analytics to support marketing strategies across onboarding, engagement, deepening, retention, and win-back - leveraging customer data and models.
• Identify key behavioral signals and lifecycle triggers that indicate opportunities for proactive marketing outreach, offer delivery, or retention intervention.
• Support account management targeting programs - including balance conversion, retention, and cross-sell - by translating customer-level analytics into actionable campaign inputs.
• Track and report on customer metrics - including engagement, activation, utilization, and attrition risk - providing regular insights to Partnership Marketing stakeholders.
• Collaborate with PDM analytics and data science teams to leverage next-best-action and next-best-offer model outputs in customer management campaigns.

• Design, execute, and analyze test-and-learn programs across DM, acquisition, and account management - including A/B tests, holdout groups, and multivariate experiments.
• Develop a structured test-and-learn roadmap aligned to Partnership Marketing priorities, ensuring tests are designed to generate statistically valid and actionable insights.
• Define test hypotheses, sample sizes, control groups, and success metrics in collaboration with marketing and business stakeholders.
• Analyze test results with statistical rigor, communicating findings, confidence levels, and recommended actions clearly to non-technical audiences.
• Maintain a test-and-learn knowledge…