Data Scientist (Prepayment Modeling)

Overview Position Summary: Bayview is a leading financial services firm specializing in mortgage-related investments. Our Research team is dedicated to delivering cutting-edge solutions through rigorous research and advanced modeling techniques. Role Overview: Bayview Asset Management is seeking an experienced modeler to join our Prepayment Modeling Team within the Research group. The initial focus will be on developing models to predict near-term prepayment speeds for US residential mortgages using mortgage data and third-party sources such as Credit Bureau data. Over time, the role may expand to support additional modeling efforts across the platform. The ideal candidate will have a minimum of 2 years of experience in statistical modeling, with a focus on mortgages. This role involves validating and calibrating prepayment models, as well as collaborating with business teams to enhance their understanding and application of these models. Key Responsibilities: Perform statistical analysis of large data sets using Python, SQL. Build, calibrate, and validate statistical models to support business needs. Implement models in Python, C as needed for production. Interface with business side users of predictive models and effectively explain the work performed, and help the business value the assets in question. Analyze mortgage data to identify trends and patterns that influence prepayment behavior. Validate and calibrate mortgage prepayment models to ensure robustness, consistency, and predictive power. Communicate complex statistical concepts and model outputs to non-technical stakeholders. Qualifications: Bachelor’s or Master’s degree in Statistics, Mathematics, Economics, Finance, or a related field. Minimum of 2 years of experience in statistical modeling, preferably within the mortgage industry. Proficiency in Python and SQL is a must for model development and empirical analysis. Experience with object-oriented languages is a must; experience with C is preferred as our models are implemented in C . Strong analytical and problem-solving skills with attention to detail. Strong communication skills, both written and verbal, and the ability to work on multiple tasks and projects simultaneously. Ability to work independently and as part of a collaborative team. Experience with machine learning techniques and data science tools. Familiarity with mortgage data. CERTIFICATIONS, LICENSES, and/or REGISTRATION N/A. LOCATION & COMPENSATION: This role is a hybrid position (3 days onsite) based in either Bayview’s Coral Gables, FL office or NYC offices. We have existing team members in both office locations. Base compensation is expected to be $110-130k, with opportunity for incentive compensation including a performance-based bonus. PHYSICAL DEMANDS and WORK ENVIRONMENT The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions. While performing the duties of this job, the employee is regularly required to sit and use hands to handle, touch or feel objects, tools, or controls. The employee frequently is required to talk and hear. The noise level in the work environment is usually moderate. The employee is occasionally required to stand; walk; reach with hands and arms. The employee is rarely required to stoop, kneel, crouch, or crawl. The employee must regularly lift and/or move up to 10 pounds. Specific vision abilities required by this job include close vision, color vision, and the ability to adjust focus. EEOC Bayview Asset Management is an Equal Employment Opportunity employer. All aspects of consideration for employment and employment with the Company are governed on the basis of merit, competence and qualifications without regard to race, color, religion, sex, national origin, age, disability, veteran status, sexual orientation, or any other category protected by federal, state, or local law.