Senior Data Scientist

Ocrolus Inc.

13d • $165,000

About The Position

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!

Requirements

• 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)

Nice To Haves

• 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)

Responsibilities

• 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 This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Number of Employees: 501-1,000 employees