Job Description
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Company Overview
A financial technology company focused on helping leading brands increase customer lifetime value through modern co-branded credit card and payment solutions. The company combines payments infrastructure, intelligent underwriting, customer data, and technology to create personalized experiences for consumers while helping brand partners develop efficient and profitable financial programs.
Responsibilities
- Own and improve the top-of-funnel credit decisioning pipeline, including application scoring, policy rules, decline waterfalls, and approval rate optimization across multiple acquisition channels.
- Build and iterate on underwriting, targeting, and segmentation models designed to expand safe approvals and improve acquisition quality.
- Design and analyze A/B tests and champion/challenger experiments for credit policies, establishing structured test-and-learn processes across acquisition and credit performance.
- Develop channel-level performance models connecting application volume with downstream economics, including approval rates, expected losses, lifetime value (LTV), customer acquisition cost (CAC), and contribution profit.
- Design and build agentic workflows and AI-powered monitoring systems capable of detecting approval-rate anomalies, diagnosing score drift and population-mix changes, and recommending policy adjustments.
- Partner with Credit Strategy, Product, Engineering, and Marketing teams to develop targeting criteria and risk frameworks for new and emerging acquisition channels.
- Build segmentation frameworks to identify underserved populations where access to credit can be responsibly expanded.
- Analyze complex financial data to identify performance changes, investigate root causes, and develop actionable recommendations.
- Communicate analytical findings and recommendations clearly to technical and non-technical stakeholders, including senior leadership and external partners.
Qualifications
- 5–8+ years of experience in data science, risk analytics, or a related quantitative field, ideally within a high-growth startup or financial technology environment.
- Strong Python and SQL skills, with the ability to build predictive models, transform raw data, and create custom datasets from complex financial information.
- Experience developing credit risk, targeting, scorecard, underwriting, segmentation, or similar predictive models in a regulated environment.
- Strong understanding of statistical inference, experimentation design, and causal analysis, including the ability to distinguish policy effects from population shifts and channel-mix changes.
- Familiarity with AI tools and AI-native workflows, with an interest in building AI-powered analytical systems.
- Strong full-stack problem-solving skills and the ability to work through complex or incomplete datasets to identify root causes and challenge assumptions.
- Ability to present complex analytical findings clearly to both technical and non-technical audiences.
- Experience communicating with senior leadership and external partner stakeholders.
- Ability to own projects end-to-end in a fast-moving environment with limited scaffolding.
- Strong cross-functional collaboration skills, particularly with Policy, Strategy, Product, and Engineering teams.
Preferred Qualifications
- Experience with credit card underwriting, lending, or consumer credit products.
- Familiarity with credit bureau data, including Vantage, FICO, and tradeline attributes, as well as alternative data sources.
- Experience building or scaling experimentation infrastructure for credit policy testing.
- Exposure to fraud detection, KYC/IDV workflows, or application fraud models.
- Understanding of acquisition channel economics and experience partnering with marketing or credit strategy teams on targeting and LTV modeling.
Compensation
$170,000–$200,000, plus equity.
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