Sr Marketing Analyst

We need to output HTML fragment with sections: About the role? The posting includes a paragraph about company mission, then "Job Purpose" paragraph. Then "Duties and Responsibilities" list. Then "Minimum Qualifications" list. Then "Preferred Qualifications" list. Also there is a note about California Employee Privacy Policy and EEO boilerplate which we must remove. Also remove application instructions (none). Also remove duplicated lines. Also remove cookie/navigation/legal/EEO etc. We must not repeat job title as heading. The job title is Senior Marketing Analyst (implied). We must not put Job ID etc. We need sections only when posting has real specific content. We have content for About the role? The initial paragraph about company mission could be considered "About the role" or maybe "About the company". The instruction says use

for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). So we can have sections: About the role (maybe summarizing the mission and job purpose). Responsibilities (duties). Requirements (minimum qualifications). Preferred Qualifications could be under Skills? But they said we can have Skills section. However we have Preferred Qualifications list; we could treat as Skills. But better to have a section "Preferred Qualifications" maybe not in list but we can use "Qualifications" for minimum and "Preferred Qualifications" as separate? The instruction says use headings like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. It doesn't forbid other headings? It says use

for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). Likely they expect those headings only. So we should map content to those headings. We have: - About the role: could include the introductory mission paragraph and Job Purpose paragraph. - Responsibilities: Duties and Responsibilities list. - Requirements: Minimum Qualifications list. - Skills: maybe combine Preferred Qualifications? Or we could put Preferred Qualifications under Skills. - Benefits, Pay, Schedule: not present, so omit. We must not create sections with filler. So we will output:

About the role

...

Then

Responsibilities

• ...

Then

Requirements

• ...

Then

Skills

• ...

(for preferred qualifications) We need to preserve all substantive information, not invent or summarize away detail. So we need to copy the text exactly as given, but we can format as list items. We must remove boilerplate: California Employee Privacy Policy link, EEO statement, etc. Also remove duplicated lines (none visible). Also remove application instructions (none). Also remove cookie/navigation/legal etc. We need to ensure we keep all substantive content from the posting: The initial motivational paragraph, Job Purpose paragraph, Duties and Responsibilities (a long paragraph with many sentences separated by periods but not bullet list). We need to turn each duty into a

. The original is a long paragraph with sentences separated by periods and semicolons. We need to split into list items preserving each duty. We must not lose any detail. Similarly Minimum Qualifications is a paragraph with sentences separated by periods. We need to split each qualification into list items. Preferred Qualifications also a paragraph with sentences. We need to be careful to not lose any content like "If you are a job applicant who resides in the state of California, please review our California Employee Privacy Policy at the following link: ..." That's boilerplate to remove. Also the EEO paragraph. Also there is a line: "Regional is an equal opportunity employer..." remove. Also there is a line: "If you are a job applicant who resides in the state of California, please review our California Employee Privacy Policy at the following link: https://regionalfinance.com/wp-content/uploads/2022/11/UPDATED-Employee-Privacy-Policy-11.2022.pdf" remove. Also there is a line: "Regional’s policy of non-discrimination applies to all phases..." remove. Thus final HTML includes only the sections. We need to ensure we don't include any extra whitespace or markdown. Let's extract the content. First paragraph: "Take your career to the next level! In the last few years our goal has been expansion, creating growth opportunities for many of our team members. Not only are we serious about growth, but we are also serious about helping our customers during hard financial times. We take pride in providing solutions and offering a helping hand, not only to our customers but also to the communities we serve. As we continue to expand and grow into a national leader in consumer financing, we invite you to consider joining our team. If you're passionate about making a meaningful impact in people's lives and bringing a personal touch to finance, we'd love to have you on board!" Second paragraph: "Job Purpose The 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." We can combine these two paragraphs under About the role. Now Duties and Responsibilities: It's a long paragraph starting with "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." We need to split into list items. Each sentence ends with a period. Some sentences have semicolons inside but still part of same sentence. We'll split by period followed by space or end. Let's manually create list items: 1. 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. 2. Build customer and prospect segmentation, targeting, offer, channel, and contact-strategy recommendations using credit-bureau, first-party, digital, geographic, and operational data. 3. 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. 4. 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. 5. 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. 6. Design and evaluate champion/challenger tests, holdouts, A/B and multivariate experiments; determine sample sizes; quantify incrementality and statistical significance; and recommend scalable actions. 7. 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. 8. Create and maintain trusted dashboards, scorecards, forecasting tools, and executive reporting in Python, Sigma, or comparable platforms, with governed definitions and drill-down capability. 9. Work with cloud data platforms such as Snowflake and with data-engineering partners to improve data models, lineage, quality, accessibility, and self-service analytics. 10. 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. 11. Perform geographic and market-potential analysis to support branch territories, digital lead routing, expansion, consolidation, and new-market planning while applying consistent, documented criteria. 12. Use approved generative-AI assistants and agentic workflows to accelerate coding, testing, data preparation, research, documentation, reporting, and scenario analysis. 13. Identify and implement automation opportunities that reduce cycle time and manual effort. 14. Present concise, decision-ready insights to senior leaders and cross-functional partners, clearly distinguishing facts, assumptions, uncertainty, risks, and recommended actions. 15. Mentor analysts, promote reusable code and disciplined analytics practices, and continuously evaluate emerging data, cloud, visualization, and AI capabilities. Now Minimum Qualifications paragraph: "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,