Bring your expertise to a Home Lending Data and Analytics team that partners closely with product, marketing, and sales to solve complex problems. You will work in a fast-paced, matrixed environment where rigorous analysis and practical recommendations promote measurable outcomes. You will help improve customer experience by identifying opportunities, clarifying trade-offs, and informing decisions with data. If you enjoy hands-on analytics with real business impact, this role is for you.
Job summary
As a Senior Associate Data Scientist in Home Lending Data and Analytics supporting the Originations Product team, you translate data into clear insights and recommendations that guide product decisions and business outcomes. You define success metrics, validate data, and deliver measurement that stakeholders can trust. You partner with analytics and predictive modeling teams to enable reliable analysis and model development through strong data identification and quality checks. You communicate findings effectively to technical and non-technical audiences so decisions are timely and well-supported. Job Posting Standards and Components.pdf
Job responsibilities
- Identify and quantify obstacles to business goals using analytics and data science techniques
- Analyze data to uncover trends, drivers, and patterns and translate findings into clear narratives that inform decisions
- Define success metrics and measurement approaches that align to product goals and stakeholder needs
- Validate data sources and perform data quality checks to enable reliable analysis and model development
- Extract, transform, and analyze data from multiple sources using SQL to produce reproducible datasets
- Partner cross-functionally to identify friction points and opportunities and recommend practical improvements
- Surface data-driven opportunities to improve efficiency, reduce friction, and enhance customer experience
- Gather requirements, clarify business questions, and align stakeholders on analytical approach and deliverables
- Deliver timely analyses and summaries tailored to the audience and decision context
- Document work, share best practices, and contribute to team standards for rigor, quality, and learning
- Maintain strong attention to detail and analytic integrity in a fast-paced environment Job Posting Standards and Components.pdf
Required qualifications, capabilities, and skills
- Three years of experience in analytics, data science, business intelligence, or a related quantitative role
- Strong SQL skills, including complex querying, joins, aggregations, and data validation
- Proficiency in at least one of the following: Python, SAS, or R
- Demonstrated ability to translate analysis into actionable insights and recommendations
- Strong written and verbal communication skills for technical and non-technical audiences
- Strong analytical and critical thinking skills, with the ability to connect detail to business context
- Ability to manage multiple priorities, work independently, and deliver high-quality work under time constraints
- Strong interpersonal skills and experience collaborating across teams and functions Job Posting Standards and Components.pdf Job Posting Standards and Components.pdf
Preferred qualifications, capabilities, and skills
- Experience in mortgage banking or financial services
- Experience in stakeholder-facing analytics, consulting, or strategy work in ambiguous problem spaces
- Experience supporting change initiatives or operational and process improvements through analytics
- Experience designing, supporting, or evaluating tests or experiments using basic statistical concepts
- Demonstrated interest in continuous learning and adopting new analytical approaches, tools, or methods
Preferred Background
- Experience in Mortgage Banking or Financial Services is preferred but not required.
- Exposure to consulting, strategy, or stakeholder-facing analytics work is a plus, particularly where requirements are ambiguous and problems are not well-defined.
- Experience supporting change initiatives or operational/process improvements through analytics is beneficial.
- Demonstrated interest in continuous learning and adopting new analytical approaches, tools, or methods to improve outcomes.
- Experience designing, supporting, or evaluating tests or experiments using basic statistical concepts.