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Salary: USD 230,000 - 270,000 / annual
Ocrolus is an AI-powered fintech platform that helps lenders automate workflows and make faster, more accurate lending decisions. The company processes nearly one million credit applications monthly and is trusted by over 400 customers including Better Mortgage, Brex, PayPal, Plaid, and SoFi.
The Small Business team leverages Ocrolus's unique dataset of cash flow, credit, and financial data to build high-quality data products for lenders. As SMB Analytics & Data Science Lead, you will lead efforts to mine internal, client, and partner data for insights and develop data products, predictive models, and analytical tools that drive credit, fraud, and operational risk decisions.
Key responsibilities include:
- Analyze large, diverse datasets around business cash flow, financial health, credit, marketplace dynamics, and repayment performance to identify product opportunities
- Partner with Product, Engineering, and senior management to develop and commercialize analytical and data science products
- Lead Ocrolus's analytical and data science research efforts, mentoring colleagues on business and technical topics
- Serve as subject matter expert with clients and prospects on small business credit and Ocrolus analytics products
- Develop robust, scalable, and efficient models balancing algorithmic complexity, interpretability, customer needs, and delivery timelines
- Translate ambiguous business challenges into well-defined data science problems
- Own the end-to-end lifecycle of data science models from exploration and feature engineering through deployment, monitoring, and continuous improvement
- Example initiatives: agentic pipelines for transaction classification, gradient boosting models for loan default prediction, entity resolution systems, fraud detection systems, and comprehensive small business financial health profiling
Requirements:
- 7+ years of professional experience in risk management, analytics, and/or data science, building decision strategies and deploying predictive models in production
- Significant knowledge of small business credit, including underwriting, pricing, and portfolio management
- Expertise and hands-on experience with LLMs and cutting-edge AI tools
- Full-stack data science/analytics experience: ideating, building, deploying, monitoring, and maintaining production ML models
- Deep understanding of statistics, probability, and machine learning algorithms
- Strong software engineering and data engineering fundamentals; excellent Python programming and proficiency with pandas, scikit-learn, Hugging Face
- Excellent SQL skills and comfort with large, complex data warehouses (Snowflake/Postgres)
- Bachelor's or Master's degree in a quantitative discipline (Computer Science, Statistics, Math, Engineering)
- Ability to communicate and present complex technical topics to various audiences; skilled in collaborative communication with clients and prospects
- Passion for understanding the "why" of problems and impact on client outcomes
Bonus: Experience with messy, real-world financial data (bank transactions, financial statements, credit reports); portfolio of past data science/analytics work with source code.
