Job PurposeThe Senior Marketing Analyst turns complex market, customer, credit, campaign, and operational data into profitable growth strategies for our personal loan business. This role owns advanced analytics across direct mail, prequalified offers, digital and affiliate leads, and customer lifecycle programs; builds reliable data products and measurement frameworks; and partners with Credit Risk, Finance, Operations, Compliance, and Technology to improve acquisition economics, credit quality, customer experience, and return on capital. The analyst is expected to work independently, automate repeatable processes, communicate clearly with senior leaders, and use modern AI tools responsibly to accelerate coding, analysis, documentation, and insight generation without compromising accuracy, privacy, security, or regulatory obligations.
Duties and Responsibilities- Own end-to-end measurement and optimization of direct-mail, prequalified, digital, affiliate, email, and retention campaigns using response, conversion, booked volume, cost per booked loan, lifetime value, net credit loss, contribution margin, and return-on-capital metrics.
- Build customer and prospect segmentation, targeting, offer, channel, and contact-strategy recommendations using credit-bureau, first-party, digital, geographic, and operational data.
- Develop reproducible production workflows leveraging Python, AI tools like Claude, and SQL to score audiences, apply eligibility and suppression rules, assign offers and branches, create mail and lead files, and generate selection waterfalls and forecasts.
- Establish automated quality controls, reconciliations, peer-review checks, documentation, and audit trails so every campaign population, exclusion, offer, and downstream file is complete, accurate, explainable, and reproducible.
- Measure the full customer lifecycle-including application, approval, funding, renewal, retention, delinquency, loss, and profitability-and translate findings into changes to targeting, pricing, offers, and channel strategy.
- Design and evaluate champion/challenger tests, holdouts, A/B and multivariate experiments; determine sample sizes; quantify incrementality and statistical significance; and recommend scalable actions.
- Analyze digital and affiliate funnels from lead through funded loan, identify conversion and credit-performance drivers, evaluate partner quality, and recommend changes to filters, pricing, bids, routing, and spend.
- Create and maintain trusted dashboards, scorecards, forecasting tools, and executive reporting in Python, Sigma, or comparable platforms, with governed definitions and drill-down capability.
- Work with cloud data platforms such as Snowflake and with data-engineering partners to improve data models, lineage, quality, accessibility, and self-service analytics.
- Build, validate, monitor, and explain predictive models for response, conversion, propensity, attrition, and profitability; partner with Credit Risk on the appropriate use of risk, fraud, and pricing models.
- Perform geographic and market-potential analysis to support branch territories, digital lead routing, expansion, consolidation, and new-market planning while applying consistent, documented criteria.
- Use approved generative-AI assistants and agentic workflows to accelerate coding, testing, data preparation, research, documentation, reporting, and scenario analysis.
- Identify and implement automation opportunities that reduce cycle time and manual effort.
- Present concise, decision-ready insights to senior leaders and cross-functional partners, clearly distinguishing facts, assumptions, uncertainty, risks, and recommended actions.
- Mentor analysts, promote reusable code and disciplined analytics practices, and continuously evaluate emerging data, cloud, visualization, and AI capabilities.
Minimum Qualifications- Bachelor's degree in analytics, statistics, data sciences, economics, finance, marketing, computer science, information systems, or a related quantitative field; equivalent relevant experience may be considered.
- Four or more years of progressively responsible experience in marketing analytics, customer analytics, risk analytics, data science, or a related discipline, including ownership of complex analyses from question through recommendation.
- Advanced SQL and strong Python skills for data manipulation, automation, statistical analysis, visualization, and production-quality workflows; experience with Git-based version control and code review.
- Demonstrated experience working with large, complex datasets in a modern cloud data platform such as Snowflake or a comparable environment.
- Strong grounding in experimental design, causal thinking, inferential statistics, forecasting, segmentation, and predictive modeling.
- Experience developing governed dashboards and decision tools in Sigma or comparable business-intelligence platform.
- Strong working knowledge of approved generative-AI tools for coding, analytics, research, documentation, workflow automation, and quality assurance, including prompt design, output validation, hallucination detection, privacy safeguards, and responsible-use practices.
- Ability to understand lending economics and connect marketing decisions to approval, funding, revenue, credit loss, lifetime value, contribution margin, and return on capital.
- Excellent written and verbal communication skills, with demonstrated ability to influence business partners and present complex analysis to senior leaders.
- High standards for accuracy, documentation, reproducibility, data stewardship, and independent judgment in a regulated environment.
Preferred Qualifications- Experience in consumer finance, personal lending, credit cards, fintech, or other financial-services environment.
- Hands-on experience with direct-mail prescreen or prequalification programs, credit-bureau data, firm-offer campaign controls, suppression logic, and mail-house or fulfillment partners.
- Experience with digital lead generation, affiliate marketplaces, web analytics, attribution, funnel optimization, and marketing-partner performance management.
- Experience with machine-learning techniques and libraries such as pandas, numpy, Polars, scikit-learn, XGBoost, or comparable tools; familiarity with model monitoring, explainability, and governance.
- Experience with dbt, orchestration tools, APIs, CI/CD, data catalogs, or automated testing within an analytics engineering environment.
- Experience with geospatial analysis, market sizing, branch or territory optimization, and fair-lending-aware geographic methodologies.
- Experience creating semantic models, executive dashboards, and self-service reporting in Sigma, a comparable platform.
- Graduate degree or relevant certification in analytics, data sciences, statistics, computational finance, finance, computer science or AI is a plus.
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