Note: The job is a remote job and is open to candidates in USA. Ocrolus builds AI workflow and analytics products for lenders. The Lead Credit & Risk Data Science / Analytics role leads the development and commercialization of data products, predictive models, and analytical tools for small business credit, fraud, and operational risk. It also involves analyzing large datasets and partnering with clients and internal teams to bring products to market.
Responsibilities
- Analyze large, diverse, and unique datasets around business cash flow, financial health, credit, marketplace dynamics, and repayment performance to unearth powerful insights and identify compelling product opportunities
- Partner with Product, Engineering, senior management, and other stakeholders to develop and commercialize analytical and data products that will drive impact for our clients and gain adoption in the market
- Lead Ocrolus’ analytical and data science efforts to research and develop new data and decisioning products, including mentoring colleagues from a business and technical standpoint
- Interact with clients and prospects as a subject matter expert on small business credit and the Ocrolus suite of analytics and decisioning products
- Develop robust, scalable, and efficient models, thoughtfully balancing algorithmic complexity against 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 data exploration and feature engineering to deployment, monitoring, and continuous improvement in production
- Examples of SMB analytics initiatives include: using agentic pipelines to enhance transaction classification, training gradient boosting trees to predict loan default probability and loss-given-default based on transactional cash flow data, building entity resolution systems to match financial data across time with a specific merchant, mining massive internal and partner datasets to build agentic fraud detection systems, resolving data signals from across the Ocrolus network to build a comprehensive and contextualized profile of small business financial health and debt capacity
Skills
- 7+ years of professional experience in risk management, analytics, and/or data science, building decision strategies and building/deploying predictive models in a production environment
- Significant knowledge of small business credit, including underwriting, pricing, and portfolio management
- Expertise and hands-on curiosity with using LLMs and cutting-edge AI tools to create novel products and solutions
- Full stack data-science/analytics experience: ideating, building, deploying, monitoring, and maintaining production ML models that solve real-world needs
- Deep understanding of statistics, probability, and machine learning algorithms
- Strong software engineering and data engineering fundamentals
- Excellent 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)
- Bachelor's or Master's degree in a quantitative discipline (e.g., Computer Science, Statistics, Math, Engineering)
- The ability to communicate and present complex technical topics and results to various audiences
- Skilled in collaborative and compelling communication with clients and prospects
- Passion for understanding the “why” of the problem and the impact of solutions on client outcomes
- Experience working with messy, real-world financial data (e.g., bank transaction streams, financial statements, credit reports)
- Portfolio of past data science/analytics accomplishments (including source code)
Benefits
- Equity
- Remote-first work environment
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