Data Scientist

Risk Modeler

Hive Financial Systems brings together decades of industry experience with sophisticated automation and technology expertise. Our focus is in the sub-prime consumer lending vertical. Consumer lending continues to be in a state of disarray in a market that has yet to reconcile with the mistakes made leading up to the financial crisis. This psychological barrier in conjunction with the current regulatory environment has contributed mightily to incredibly tight consumer underwriting standards from traditional lenders (i.e. banks) despite the fact interest rates are very low and institutional capital has found its way back into the credit market. At its simplest, the demand for alterative credit options is there, but the supply is not. It is very apparent that this market shortfall has created an arbitrage opportunity for unparalleled risk adjusted returns when compared to similar opportunities. The only question is whether market entrants have the skill and depth of knowledge to underwrite consumers by incorporating massive amounts of public and private data that has been ignored in making credit decision and if these companies can secure enough capital to effectively scale.

Job Summary:

We are seeking a skilled and detail-oriented Risk Modeler/Data Scientist to join our risk team. The ideal candidate will design, develop, and implement statistical and machine learning models to assess, monitor, and mitigate credit risk. You will play a critical role in shaping risk strategies, enhancing underwriting processes, and optimizing portfolio performance in a fast-paced, data-driven environment.

Key Responsibilities:

Model Development & Implementation:

• Build predictive models (e.g., credit scoring, probability of default, loss given default, fraud detection) using statistical techniques (e.g., logistic regression, decision trees, random forests, gradient boosting, etc.).
• Leverage machine learning techniques and advanced analytics tools to improve risk assessment, customer segmentation, and loan performance prediction.
• Validate, calibrate, and monitor models to ensure accuracy, reliability, and compliance with internal and regulatory standards.

Data Analysis & Insights:

• Analyze large datasets to identify trends, correlations, and risk drivers, ensuring data quality and consistency.
• Collaborate with data engineering teams to ensure data pipelines meet modeling requirements.
• Provide actionable insights into consumer behavior, portfolio performance, and credit trends.

Risk Strategy Development:

• Partner with underwriting, pricing, and collections teams to develop and refine risk policies and strategies.
• Develop champion/challenger strategies to continuously improve underwriting and portfolio risk management.
• Conduct portfolio stress testing and scenario analysis to assess potential risk exposures.

Compliance & Reporting:

• Ensure models and risk strategies comply with applicable regulatory requirements (e.g., ECOA, FCRA, CFPB guidelines).
• Document model development processes, assumptions, and validation methodologies for audits and regulatory reviews.
• Prepare and deliver presentations to senior management and stakeholders on model performance and risk insights.

Qualifications:

Education:

• Bachelor’s degree in Statistics, Mathematics, Data Science, Economics, Computer Science, or related field (master’s or Ph.D. preferred).

Experience:

• 3-5 years of experience in risk modeling, preferably in consumer lending or financial services.
• Proven experience in building and validating credit risk models or related predictive analytics.

Technical Skills:

• Proficiency in statistical tools and programming languages such as Python, R or similar.
• Experience with machine learning libraries (e.g., scikit-learn, TensorFlow, XGBoost).
• Familiarity with big data platforms (e.g., Hadoop, Spark) and cloud environments (e.g., AWS, Azure).
• Strong knowledge of SQL for data extraction and manipulation.

Preferred Experience:

• Hands-on experience working with Azure Machine Learning or DataRobot for model development and deployment.
• Familiarity with Databricks or Microsoft Fabric for data engineering and collaborative analytics.
• Knowledge of consumer lending products, underwriting processes, and regulatory frameworks.
• Hands-on experience in fraud detection or portfolio risk optimization.

Other Skills:

• Strong analytical and problem-solving skills with a keen attention to detail.
• Excellent communication and presentation skills, with the ability to convey technical findings to non-technical audiences.
• Ability to work collaboratively in a cross-functional team environment.