JD Case Study · Citizens
Head of Decisioning Analytics & Optimization
A "Head of" role owning decisioning analytics and the optimization framework for a card portfolio — segmentation, statistical models, test-and-control, account-level profitability. This is exactly the decision-management and optimization engine I built (funnel, experimentation, LTV), now at portfolio scale.
What they’re actually buying
Build the decision-optimization layer that lifts portfolio ROI. Model the decisions — who to target, approve, and how to treat them — prove them with test-and-control, and wire the whole thing into an optimization framework that improves campaign and channel returns. Optimize the funnel, not one campaign at a time.
How I’d own it — first 90 days
Days 1–30
Map the decisions
Inventory the decision points (targeting, approval, treatment, retention) and their KPIs. Baseline account-level profitability by segment, and meet the Marketing, Risk, and Analytics partners.
Days 31–60
Stand up the optimization diagnostic
Ship a decisioning dashboard that shows the targeting → approval → activation → profitability chain by segment, and pinpoints where the framework is leaving ROI on the table.
Days 61–90
Run the first optimization experiment
Design a test/control on a decisioning change with profitability guardrails, deliver the business case in dollars, and stand up the experimentation governance and optimization cadence.
Signature analysis: the portfolio decision funnel
The optimization insight: the stages aren’t independent. Tightening approval to cut losses can starve activation and profitability; loosening targeting floods the funnel with low-value accounts. The job is optimizing the chain jointly against account-level profitability — which is what a real decisioning framework does, and a campaign-by-campaign view never will.
Illustrative figures — a portfolio decision funnel optimized end-to-end against profitability, not stage by stage.
Requirement → proof
| What the JD asks for | What I’ve already shipped |
|---|---|
| Decisioning analytics & optimization framework | A full decision funnel diagnostic that locates and sizes the ROI leak. |
| Test & control, statistical / segmentation models | An A/B + incrementality readout with guardrails and a sample-size calculator. |
| Account-level profitability / LTV | An LTV / unit-economics model where the loss rate is the central optimization lever. |
| SQL / SAS, advanced analytical tools | Show-the-SQL blocks, channel-aware, warehouse-ready. |
| Card / consumer-credit portfolio domain | Competitive spotlights across the consumer-credit and BNPL field. |
The same engine
The engine behind this portfolio is a decision-optimization framework: a funnel modeled end-to-end, experiments with profitability guardrails, and an LTV model that prices every trade-off. I built it for a lease portfolio; on a card book the levers are targeting, approval, and treatment — same machine, same job.
See the engine →Decisioning is optimizing the whole funnel against profit, not tuning one campaign — and I’ve shipped exactly that, in public. Let’s talk about your ROI frontier. — Paul Brown