Overview In this role you turn complex market, customer, credit, campaign, and operational data into profitable growth strategies for Regional's personal loan business. You own advanced analytics across direct mail, digital and affiliate channels, and customer lifecycle programs, building reliable data products and measurement frameworks. You collaborate across Credit Risk, Finance, Operations, Compliance, and Technology to improve acquisition economics, credit quality, and return on capital. You work independently to automate repeatable processes and responsibly use AI tools to accelerate insights while safeguarding privacy and regulatory obligations.
Responsibilities- Own end-to-end measurement and optimization of campaigns across direct-mail, prequalified, digital, affiliate, email, and retention channels using key metrics (response, conversion, booked loans, LTV, net credit loss, contribution margin, return on capital)
- Build segmentation, targeting, offer, channel, and contact-strategy recommendations using diverse data (credit-bureau, first-party, digital, geographic, operational)
- Develop reproducible production workflows with Python, AI tools like Claude, and SQL to score audiences, apply rules, assign offers, generate files and forecasts
- Establish automated quality controls, reconciliations, peer-review, documentation, and audit trails for campaign data and downstream files
- Measure full customer lifecycle and translate findings into targeting, pricing, offers, and channel strategy adjustments
- Design and evaluate tests (A/B, multivariate, champion/challenger), determine sample sizes, and assess incrementality and significance
- Analyze digital and affiliate funnels to diagnose drivers, optimize pricing, bids, routing, and spend
- Create dashboards, scorecards, forecasting tools, and executive reports in Python, Sigma, or similar platforms with governed definitions
- Collaborate with cloud data platforms (Snowflake) and data-engineering partners to improve data models, lineage, quality, and self-service analytics
- Build, validate, monitor, and explain predictive models for response, conversion, propensity, attrition, and profitability; work with Credit Risk on risk, fraud, and pricing models
- Perform geographic and market-potential analyses to support branch territories, digital lead routing, and new-market planning
- Leverage approved generative-AI assistants to accelerate coding, testing, data prep, research, and reporting
- Identify automation opportunities to reduce cycle time and manual effort
- Present concise, decision-ready insights to senior leaders and cross-functional partners, distinguishing facts, assumptions, uncertainty, risks, and actions
- Mentor analysts and promote reusable code and disciplined analytics practices; evaluate emerging data, cloud, visualization, and AI capabilities
Key requirements- Four or more years of progressively responsible experience in marketing analytics, customer analytics, risk analytics, data science, or related discipline
- Advanced SQL and strong Python for data manipulation, automation, analysis, visualization, and production workflows; Git-based version control and code review experience
- Experience with large, complex datasets on a modern cloud data platform (e.g., Snowflake)
- 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 BI platform
- Strong knowledge of approved generative-AI tools for coding, analytics, research, documentation, workflow automation, and QA including privacy safeguards and responsible-use practices
- Ability to connect marketing decisions to lending economics (approval, funding, revenue, credit loss, LTV, contribution margin, return on capital)
- Excellent written and verbal communication, with the ability to influence senior leaders; high standards for accuracy, documentation, reproducibility, and data stewardship in a regulated environment
- strong communication
- influencing and collaboration with senior stakeholders
- attention to detail
- Python
- SQL
- Snowflake
