Freddie Mac · 5 days ago

ERM Quantitative Analytics Director

Dallas, TX

Full-time

Onsite

Director

$212K/yr - $318K/yr

10+ years exp

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FinanceFinancial Services

Insider Connection @Freddie Mac

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Responsibilities

Reviewing analytical methods and quantitative models for new and existing financial and mortgage products/portfolio.

Providing resolutions to an extensive range of complicated problems; solutions are innovative, thorough, and practicable.

Acts as the Model Risk lead for model testing and validation.

Developing validation reports based on rigorous theoretical and analytical reviews of models.

Acting as an advisor and leader for subordinates to meet schedules and/or resolve technical problems as well as developing and coordinating performance requirements.

Managing the coordination of the activities of a section or department. Often must lead joint effort among with members of different team.

Manage validation of credit decisioning models. Participate in ERM’s credit strategy review/challenge process.

Manage validation of models built using modern ML/AI techniques (Neural networks, Random Forest, and various boosting algorithms).

Qualification

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ProgrammingMatlabSTATAPythonC++SASRRegression ModelsStochastic Process ModelingMonte-Carlo SimulationOptimizationEconometric TechniquesStatistical TechniquesSurvival AnalysisProportional HazardsAIML TechniquesNeural NetworksRandom ForestXGBoostFinancial Sector ExperienceProblem-SolvingInterpersonal Communication

Required

MS in Economics, Finance, Statistics, Engineering, MBA with a concentration in Finance, Operational research, or a directly related quantitative field with at least 10 years of post-graduate work experience in quantitative analytics or modeling

Programming experience with a good level of knowledge of at least one: Matlab, STATA, Python, C++, SAS, R

Deeply knowledgeable of regression models, stochastic process modeling, Monte-Carlo simulation, Optimization and/or other econometric and statistical techniques such as survival, proportional hazards, etc.

Knowledge of AIML techniques such as neural networks, random forest, XGBoost and similar algorithms

Experience in the financial sector preferred

Ability to work across teams and with diverse organizations of modeling and risk professionals in an effective and efficient manner