Staff Data Scientist

Self; Austin (TX); Hybrid; USD 140,000 - 200,000; Full time

Benefits offered by this job

Health insurance

Equity stock options

Relocation bonus to move to Austin

Hybrid work arrangement

Education reimbursement program

Paid parental leave

Job summary

Self Financial is seeking a Staff Data Scientist in Austin to build, productionize, and monitor predictive models that drive credit and lending decisions. You will collaborate with Engineering, Risk, Finance, and Product to translate business questions into robust models, delivering measurable value and clear explanations.

You will mentor teammates, establish technical standards, and work in a hybrid or remote setup in Austin. 8+ years of experience and strong communication skills are essential.

Qualifications

• 8+ years of professional data scientist experience.
• Strong Python skills and a track record of building predictive models.
• Demonstrated statistical rigor with measurable impact.
• Ability to quantify business impact in dollars and percentages.
• Experience taking models from development to production.

Responsibilities

• Design, build, and validate predictive models in Python for credit and lending decisions.
• Partner with engineering to deploy models into the decisioning engine and monitor post-launch.
• Analyze data to understand model behavior and ensure validity.
• Quantify and communicate business impact of your work with stakeholder teams.
• Collaborate with Risk, Finance, Product, and other stakeholders to translate questions into modeling problems.
• Evaluate data vendors and tools used by the team.
• Set technical standards and mentor other analysts and data scientists.

Skills

Python

Predictive modeling

Statistical rigor

Business impact

Model deployment

Communication

Job description

About Self Financial

Self Financial is a venture-backed, high-growth FinTech company with a mission to increase economic inclusion and financial resilience by empowering people to build credit and build savings.

We challenge the status quo of the credit industry by giving people accessible tools to take control of their credit, with a focus on the 100 million+ Americans who have no or low credit. We're looking for people who share our passion, tackle challenges, find solutions, and make the financial space better for the communities we serve.

Title

Staff Data Scientist

Team

Credit Analytics. Builds the predictive models that drive our credit and lending decisions. We turn deep knowledge of our data into models that are deployed in production, and we work closely with Risk, Finance, Product, and Engineering to improve how we underwrite, price, and manage our customers’ credit.

Location: Austin, TX

Work Arrangement: Austin – Hybrid or Remote

About the Role

You’ll build the models, take them into production, and show what they changed for the business. We’re a small company, so you’ll wear many hats and work with engineering to put your models into our decisioning engine. We care less about “here’s the model and here’s what it does” and more about the dollars and percentages it moved, the context behind it, and how clearly you explain it.

What you’ll do:

• Design, build, and validate predictive models in Python that inform credit and lending decisions
• Partner with engineering to deploy models into our decisioning engine and monitor them after launch
• Dig into the data to understand why a model behaves the way it does, and keep digging until the answer holds up
• Measure and communicate the business impact of your work in dollars and percentages
• Work with Risk, Finance, Product, and other stakeholders to turn business questions into modeling problems and recommendations
• Evaluate and work with the data vendors and tools we use
• Set the technical bar for the team through code and model review, and mentor other analysts and data scientists

What You Bring

• 8+ years of professional data scientist experience
• Strong Python skills and a record of building predictive models
• Statistical rigor, shown in at least one accomplishment on your resume
• Business impact you can quantify, with the dollars and percentages to show it
• Curiosity that goes past “here’s a cool model,” and the habit of asking why until you understand the context
• Clear communication with both technical and non-technical audiences
• Experience taking models from development to production

What Separates You

• Experience in credit and lending with fintech as a plus
• Experience working with third-party data vendors
• Experience at a small or scaling company where you owned work end to end
• Comfort moving between hands-on modeling, stakeholder partnership, and mentoring

Benefits & Perks

• Generous health, dental, and vision insurance coverage
• Employer-paid benefits including life, disability, and cost-containment resources
• Hybrid work arrangement
• Flexible vacation
• Company performance-based bonuses
• Paid parental leave
• Company equity in the form of stock options
• Education reimbursement program
• Relocation bonus to move to Austin
• Gym membership options through ClassPass or monthly reimbursement
• Lunchdrop stipend
• Paid parking
• ERG and inclusion opportunities
• Casual dress code
• Team building events and activities
• Referral program

Equal Opportunity Employer

Self Financial is an Equal Opportunity Employer. We celebrate diversity and are committed to an inclusive environment. We do not discriminate on the basis of any protected group status under applicable law. If you need an accommodation during the application or interview process, please let us know.

Work Authorization

We can only consider candidates who are U.S. citizens or permanent residents (green card holders). We are unable to offer visa sponsorship of any kind, now or in the future. All new hires must successfully pass a background check.

AI Integrity Policy

Self Financial is an AI-forward company. Our interview process is designed to understand your own career experience, technical acumen, and communication skills. Please do not use AI tools to generate or assist with your answers during interviews unless we explicitly invite you to.