Statistical Modeling Analyst Jobs (NOW HIRING)

Senior Credit Risk Modeling Analyst will have the ability to work a hybrid schedule (remote/onsite) aftera period of training (time frame may vary). Training will take place at the RBFCU Administrative Service Center: 1 Ikea-RBFCU Pkwy, Live Oak, Texas 78233.

All applicants must reside within the state of Texas and have the capability of performing all of the work from their home in Texas.

To successfully work from home, employees must have access to a minimum internet connection as noted by RBFCU.

• Must have a reliable home internet provider and the ability to hard wire a connection directly to modem (Ethernet cable provided)

• Must be able to provide a workspaces at home that is safe, suitable for work, and within a distraction free environment

The Senior Credit Risk Modeling Analyst will design and build credit risk models that enable automated underwriting while optimizing decision rates and loss performance within defined risk tolerances. Apply statistical and machinelearning techniques to large, complex datasets for feature engineering, model development, and loss forecasting, with consideration for governance and regulatory requirements. Continuously monitors model performance and input stability to detect variable drift and emerging risk as origination strategies evolve.

Essential Functions and Responsibilities:

• Utilizes risk modeling techniques to identify, quantify, and forecast potential credit risk and opportunities for the institution

• Develops and maintains expertise in the fields of risk quantification and modeling to support both internal and external stakeholders

• Collaborates with stakeholders to understand product characteristics used for modeling while assisting in communication and education of current and expected risk exposures

• Makes recommendations to management on current and future strategies and profitability projections

• Perform other quantitative analysis for institution stakeholders as needed

• Leverage expertise to foster and expand collective knowledge within the team

• Gathers and analyzes pertinent data to create or strengthen models that forecast risk exposure and help make informed business decisions

• Continuously monitor the economic and business environments to update models as new data becomes available

• Defines, documents, and summarizes methodologies, assumptions, and results of risk models and prepares reports for management

• Act as a liaison between lending and IT to assist in the aggregation and organization of institutional data for the use in models and reporting.

• All other duties as assigned (note: essential functions and responsibilities may change, or new ones may be assigned at any time with or without notice)

Requirements:

• Master's degree in finance, statistics or other quantitative field or 6 years of job-related experience in lieu of master's degree

• Minimum 5 years of experience in a similar role or experience in similar areas in the Banking/Financial Services Industry

• Strong analytical, mathematics, organizational, and planning skills

• Ability to articulate complex theories, concepts, methodologies, and findings in a non-technical manner to a non-technical audience

• Innovative self-starter with ability to meet deadlines, work independently, and think outside the box

• Excellent interpersonal skills, with a desire to pursue best practices in a challenging team environment

• Proficient to advanced knowledge of statistical modeling and other quantitative techniques including, but not limited to, linear & non-linear regression, optimization, simulation, time-series analysis, probability theory, survival analysis, and value-at-risk

• Knowledgeable of modeling systems and/or computer programming languages used for modeling (e.g. python & R)

• Ability to complete multiple projects and meet deadlines

• Capable of working on assignments with minimal assistance

All qualified applicants will receive consideration for employment without regard to race, color, sex, sexual orientation, gender identity, religion, national origin, disability, veteran status, or other legally protected status.