Similar Jobs
.png){kind=logo}
.png)
Come build at the intersection of AI and fintech. At Ocrolus, we’re on a mission to help lenders automate workflows with confidence—streamlining how financial institutions evaluate borrowers and enabling faster, more accurate lending decisions.
Our AI-powered data and analytics platform is trusted at scale, processing nearly one million credit applications every month across small business, mortgage, and consumer lending. By integrating state-of-the-art open- and closed-source AI models with our human-in-the-loop verification engine, Ocrolus captures data from financial documents with over 99% accuracy. Thanks to our advanced fraud detection and comprehensive cash flow and income analytics, our customers achieve greater efficiency in risk management, and provide expanded access to credit—ultimately creating a more inclusive financial system.
Trusted by more than 400 customers—including industry leaders like Better Mortgage, Brex, Enova, Nova Credit, PayPal, Plaid, SoFi, and Square—Ocrolus stands at the forefront of AI innovation in fintech. Join us, and help redefine how the world’s most innovative lenders do business.
The data science team at Ocrolus builds high-quality, impactful analytics and machine-learning based products that empower lenders to make better credit, fraud, and operational risk decisions. Data scientists play a critical role in the full product development cycle and move fast to ideate, build, deploy, and maintain production quality models. If you are a data scientist looking to leverage your strong engineering abilities in building ML models end-to-end, then we want to talk to you!
What you’ll do
- Partner with Product, Engineering, and other stakeholders to translate ambiguous business challenges into well-defined data science problems
- Own the end-to-end lifecycle of data science models, from data exploration and feature engineering to deployment, monitoring, and continuous improvement in production
- Develop robust, scalable, and efficient models, thoughtfully balancing algorithmic complexity against interpretability, business needs, and delivery timelines
- Examples of data science initiatives include: using NLP/LLMs to classify transactions into standardized categories, training gradient boosting trees to predict loan default probability and loss-given-default based on transactional data, and building an entity resolution system to match financial documents across time with a specific borrower
What you’ll bring
- 5+ years of professional experience building and deploying machine learning models in a production environment
- Lending domain experience, applying data science principles in the management of portfolio risk or acquisition.
- Bachelor’s or Master’s degree in a quantitative discipline (e.g., Computer Science, Statistics, Finance, Math, Engineering)
- Full stack data-science experience: ideating, building, deploying, monitoring, and maintaining production ML models that solve product needs and perform with high levels of accuracy, stability, and coverage
- The ability to communicate and present complex technical topics and results to various audiences
- Passion for understanding the “why” of the problem and the impact of solutions on client outcomes
- Deep understanding of statistics, probability, and machine learning algorithms
- Strong software engineering and data engineering fundamentals
- Expert-level programming skills in Python and proficiency with core data science libraries (e.g., pandas, scikit-learn, Hugging Face)
- Excellent SQL skills and comfort working with large and complex data warehouses (Snowflake/Postgres)
- Experience with CI/CD, shell scripting, Git/version control, REST/GRPC APIs, and cloud infrastructure (AWS: S3, EKS, etc)
Bonus points
- Experience working with messy, real-world financial data (e.g., bank transaction streams, financial statements, credit reports)
- Portfolio of past data science accomplishments (including source code)
Note:
The full-time salary range for this role around $165,000 + equity + benefits. Base pay offered may vary depending on job-related knowledge, skills, experience, and market location.
Disclosure as required by N.Y.C. Admin. Code §§ 8-102 and 8-107(32) of the full time salary compensation range for this role when being hired into our offices in New York City.
Life at Ocrolus
We’re a team of builders, thinkers, and problem solvers who care deeply about our mission — and each other. As a fast-growing, remote-first company, we offer an environment where you can grow your skills, take ownership of your work, and make a meaningful impact.
Our culture is grounded in four core values:
Empathy – Understand and serve with compassion
Curiosity – Explore new ideas and question the status quo
Humility – Listen, be grounded, and remain open-minded
Ownership – Love what you do, work hard, and deliver excellence
We believe diverse perspectives drive better outcomes. That’s why we’re committed to fostering an inclusive workplace where everyone has a seat at the table, regardless of race, gender, gender identity, age, disability, national origin, or any other protected characteristic.
We look forward to building the future of lending together.
What you need to know about the Austin Tech Scene
Key Facts About Austin Tech
- Number of Tech Workers: 180,500; 13.7% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Dell, IBM, AMD, Apple, Alphabet
- Key Industries: Artificial intelligence, hardware, cloud computing, software, healthtech
- Funding Landscape: $4.5 billion in VC funding in 2024 (Pitchbook)
- Notable Investors: Live Oak Ventures, Austin Ventures, Hinge Capital, Gigafund, KdT Ventures, Next Coast Ventures, Silverton Partners
- Research Centers and Universities: University of Texas, Southwestern University, Texas State University, Center for Complex Quantum Systems, Oden Institute for Computational Engineering and Sciences, Texas Advanced Computing Center
