Lead Credit & Risk Data Science, Analytics

This is a fully remote position, open to applicants in New York.

📋 Description

• Examine extensive and varied datasets related to business cash flow, financial stability, credit, market trends, and repayment outcomes.

• Discover insights and attractive product possibilities.

• Collaborate with Product, Engineering, senior management, and stakeholders to create and commercialize analytical and data products.

• Direct analytical and data science initiatives to investigate and develop data and decision-making products.

• Guide colleagues from both business and technical viewpoints.

• Engage with clients and potential clients as a subject matter expert on small business credit and Ocrolus analytics and decision-making products.

• Construct robust, scalable, and efficient models while considering complexity, interpretability, customer requirements, and timelines.

• Convert vague business challenges into clearly defined data science problems.

• Oversee the complete lifecycle of data science models, from exploration and feature engineering to deployment, monitoring, and ongoing enhancement.

• Initiate projects including transaction classification, prediction of loan defaults and loss-given-default, entity resolution, fraud detection, and profiling of small-business financial health and debt capacity.

⛳️ Requirements

• Over 7 years of professional experience in risk management, analytics, and/or data science.

• Proven experience in developing decision strategies and deploying predictive models in a production environment.

• Extensive knowledge of small business credit, encompassing underwriting, pricing, and portfolio management.

• Proficiency in utilizing LLMs and state-of-the-art AI tools.

• Comprehensive experience in full-stack data science/analytics, covering ideation, development, deployment, monitoring, and maintenance of production ML models.

• Strong grasp of statistics, probability, and machine learning algorithms.

• Solid foundation in software engineering and data engineering principles.

• Exceptional skills in Python programming.

• Proficient in using pandas, scikit-learn, and Hugging Face.

• Excellent SQL abilities and experience with large, complex data warehouses such as Snowflake/Postgres.

• Bachelor’s or Master’s degree in a quantitative field such as Computer Science, Statistics, Mathematics, or Engineering.

• Capability to communicate and present intricate technical subjects and results to diverse audiences.

• Ability to collaborate effectively with clients and prospects.

• Experience working with messy, real-world financial data is advantageous.

• A portfolio showcasing past data science/analytics achievements, including source code, is a plus.

🏝️ Benefits

• Equity

• Benefits

• Remote-first work environment

• Inclusive workplace

• Opportunities to enhance skills

• Significant impact and ownership of work