SQL Developer

POSITION SUMMARY:

The Data Science and Analytics (DSA) team is a technical engine underpinning many of the company's key strategic functions. We are a passionate, human-centered org with diverse science/analytical skillsets working in concert.

DSA is seeking a Manager of Marketing Analytics & Data Science to support marketing strategy and execution using analytics, experimentation, and predictive modeling. This role builds and maintains analytical and predictive models that support mortgage marketing, origination, and servicing decisions across the borrower and loan lifecycle, leveraging a range of statistical and machine learning techniques (e.g., regression‑based models, decision trees, neural‑network approaches, among others) using Python or SAS. The role is hands‑on and execution‑focused, requiring strong SQL and analytics skills, an interest in marketing performance and testing, and the ability to directly support marketing operations, including building and validating campaign files. The goal is to turn data and models into clear insights and usable outputs that the business can act on quickly.

JOB FUNCTIONS AND RESPONSIBILITIES

• Design, build, and maintain predictive and analytical models to support mortgage marketing, origination, and borrower lifecycle decisions.
• Operate as a hands‑on individual contributor, delivering a steady flow of analysis, models, campaign files, and insights to meet business needs.
• Apply statistical and machine‑learning techniques (e.g., regression models, decision trees, ensemble and neural‑network‑based approaches) using Python or SAS.
• Develop and maintain SQL logic to support analytics, modeling, ETL processes, and campaign builds.
• Evaluate model performance and business impact, and improve models based on results and feedback.
• Design and analyze marketing tests (such as A/B tests and holdout groups) to measure lift and impact.
• Support strategic analysis by quickly iterating on analyses and models as business questions change.
• Partner with marketing and business teams to understand requirements and translate questions into clear analytical outputs.
• Support marketing operations by building, QA'ing, and delivering campaign files with accurate selection and suppression logic.
• Ensure analytics and models translate into usable, execution‑ready campaign outputs.
• Maintain and enhance ETL processes that prepare and reconcile marketing and contact data.
• Automate recurring analysis, data preparation, campaign builds, and QA checks to reduce manual effort.
• Maintain basic documentation of models, data logic, and analytical approaches to support consistency.
• Ensure accuracy and reliability of outputs in line with data governance expectations.
• Contribute to ongoing improvements in analytics, modeling, and automation practices within the Data Science & Analytics team.

EDUCATION / EXPERIENCE

• Minimum education required: Bachelor's degree in Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field.
• Preferred education: Master's degree in a quantitative or analytics‑focused discipline.
• Minimum years of experience required: 3–5 years in marketing analytics, data science, or analytics roles.
• Specific skills or abilities needed:
• Strong proficiency in Python for data analysis, modeling, and automation.
• Strong SQL skills, including complex joins, window functions, and subqueries.
• Experience with marketing analytics and applied data analysis.
• Familiarity with statistical or predictive modeling used for marketing or borrower behavior.
• Experience designing and analyzing marketing tests (e.g., A/B tests, holdouts).
• Ability to support marketing campaigns, including building and validating campaign files.
• Experience working with data preparation or ETL processes, such as reconciling contact data.
• Strong Excel skills, including advanced formulas and basic automation.
• Ability to manage multiple requests in a fast‑paced environment.
• Strong business acumen and ability to communicate analytical findings in clear, non‑technical terms.
• Experience translating loosely defined business questions into precise analytical deliverables.
• Interest in automating data and reporting workflows to improve efficiency and scalability.
• Experience in mortgage, financial services, or highly regulated data environments is a plus.
• Familiarity with SAS or other legacy analytics platforms is a plus.