Quantitative Analytic Senior (Hybrid - 3 Days in Office) Dallas, Texas
$118k - $178kFairygodboss
At Freddie Mac, you will do important work to build a better housing finance system and you'll be part of a team helping to make homeownership and rental housing more accessible and affordable across the nation. Position Overview Freddie Mac's Enterprise Risk Management Division is currently seeking a Quantitative Analytics Senior to join the Enterprise Model Risk Department. The department is responsible for model risk oversight, including development, validation, implementation and monitoring across multiple risk types such as credit, market and counterparty risk. This role will be part of the model validation team, providing challenge, oversight and management of the risks associated with the use of the company's models. Our Impact The team is responsible for validating econometric price models, such as home price appreciation (HPA) models, and for related model risk management activities throughout the model life‑cycle. Responsibilities Perform independent validation to evaluate model risks related to the design and conceptual soundness of the model, including all key steps in the model development process such as selection of methodology, data, sampling, segmentation, model diagnostics, model performance and outcome analysis, model overlay, and ongoing monitoring. Report findings to model owners and management and ensure those findings are addressed appropriately and in a timely manner. Prepare validation documentation in compliance with applicable policy, standards and procedures. Manage validation projects in the model inventory system collaboratively with model owners, key assessors and users. Qualifications Doctorate degree (or Master’s degree with 3 years equivalent work experience) in quantitative finance, statistics, econometrics, or a related quantitative field. Coursework or work experience applying predictive modeling techniques from finance, statistics, econometrics, mathematics, data science and computer programming to large data sets. Qualifying coursework may include statistics, mathematical programming, optimization, machine learning, computational methods, design and analysis of algorithms, Bayesian methods, derivatives, and Monte Carlo methods/modeling. Coursework or work experience writing statistical and/or optimization programs to develop models and algorithms. Programming languages may include Python, R, SQL, Java, SAS, and MATLAB. Proficiency in C/C++, Java, Python, R and SAS. Keys to Success Deep curiosity to learn with critical thinking. Strong verbal and written communication skills. Ability to work under limited direction independently to identify issues and solve problems. Current Freddie Mac employees please apply through the internal career site. Compensation and Benefits This position has an annualized market-based salary range of $118,000 – $178,000 and is eligible to participate in the annual incentive program. Freddie Mac offers a comprehensive total rewards package including competitive compensation and market‑leading benefit programs. Employment Details Position type: Full time. FLSA Status: Exempt. Equal Opportunity Statement We are an equal opportunity employer and value diversity and inclusion at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by applicable law. We will ensure that individuals with differing abilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation. California and Los Angeles County Fair Chance Qualified applications with arrest or conviction records will be considered for employment in accordance with the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. #J-18808-Ljbffr Fairygodboss
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