Senior Data Scientist

Summary

Join Ocrolus's data science team and build impactful analytics and machine-learning products that improve lending decisions. You will own the entire lifecycle of data science models, from initial concept to deployment and maintenance. This role requires strong engineering skills and experience building end-to-end ML models. The ideal candidate will have 5+ years of experience in a production environment and a deep understanding of statistics, probability, and machine learning algorithms. You will collaborate with various stakeholders to solve complex business challenges and communicate technical results effectively. Ocrolus offers a remote-first work environment and a culture focused on empathy, curiosity, humility, and ownership.

Requirements

• 5+ years of professional experience building and deploying machine learning models in a production environment
• Bachelor’s or Master’s degree in a quantitative discipline (e.g., Computer Science, Statistics, 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)

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

Preferred Qualifications

• 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)

Benefits

• Equity
• Benefits