Data Science and Artificial Intelligence Senior

Data Science and Artificial Intelligence Senior

Join to apply for the Data Science and Artificial Intelligence Senior role at Freddie Mac

At Freddie Mac, our mission of Making Home Possible is what motivates us, and it’s at the core of everything we do. Since our charter in 1970, we have made home possible for more than 90 million families across the country. Join an organization where your work contributes to a greater purpose.

Position Overview

Are you looking for an opportunity to put your technical skills to use working on advanced modeling challenges? Freddie Mac’s Single-Family Portfolio Analytics Team is currently seeking a “Data Science and Artificial Intelligence” Senior who will be responsible for accelerating machine learning and GenAI use case development and implementation for Single Family Chief Credit Officer, as well as house price models strategy and analytics to support business and risk management decisions.

Our Impact

Our team is responsible for two key areas:

• Accelerating machine learning and GenAI use case development and implementation for Single Family Chief Credit Officer, scaling of AI/ML capabilities across SF Seller Credit Risk with focus on SF portfolio credit risk analytics and modeling. Main focus is on rapid prototyping of GenAI mortgage-related products.
• Comprehensive model governance and model use strategy for House Price Models including House Price Forecasting and House Price Index models. In this role, we focus on supporting users across the company in identification and mitigation of risks related to development and implementation of House Price Models, designing practical model strategy, and supporting house price analytics.

Your Impact

• Use statistical, machine learning, and deep learning techniques to accelerate machine learning and GenAI adoption, use case development and implementation.
• Support Model Business Owner in design of practical model strategy for House Price Models and perform house price business analytics and modeling.
• Make efficient use of distributed computing technologies including Spark, Dask and others to utilize structured and unstructured big data available.
• Collect, manipulate, and analyze large volumes of data from a variety of sources and structures.
• Research latest and emerging techniques and create proof‑of‑concept projects showcasing benefits and improvements.

Qualifications

• Doctorate degree, or Master’s degree with 3 years of equivalent work experience, in quantitative finance, statistics, economics or a related quantitative field.
• Coursework or work experience designing large scale data driven experiments with focus on experimental design and sampling techniques.
• Coursework or work experience applying predictive modeling techniques from finance, statistics, mathematics, data science, or computer programming to large data sets. (e.g., statistics, mathematical programming, optimization, machine learning, computational methods, design and analysis of algorithms, Bayesian methods, derivatives, or Monte Carlo methods.)
• Coursework or work experience writing computer programs to implement data science pipelines and predictive algorithms. Programming languages may include—Python, C++, SQL, Java, SAS.
• Experience using Unix/Linux, Git, Kubernetes and related technologies as well as statistical and machine learning libraries such as scikit-learn, MLlib and Pytorch is a plus.

Keys to Success in this Role

• Exceptional quantitative and analytics skills.
• Passionate about ML/Gen AI.
• Strong focus on execution and delivery of results.
• Strong knowledge of Machine Learning and statistical models, tools and techniques.
• Strong programming skills.
• Strong written and oral communication skills.
• Ability to work under limited direction, independently resolving and developing approach to solutions.

Current Freddie Mac employees please apply through the internal career site.

We consider all applicants for all positions without regard to gender, race, color, religion, national origin, age, marital status, veteran status, sexual orientation, gender identity/expression, physical and mental disability, pregnancy, ethnicity, genetic information or any other protected categories under…