Senior Data Scientist, Ocrolus

• 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!
• 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)

Desirable

• 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
• 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