Quantitative Analytics Tech Lead – Fair Lending

Position Overview:

Are you looking for an opportunity to put your technical skills to use working on advanced modeling challenges? Freddie Mac’s Modeling, Econometrics, Data Science & Analytics Department is currently seeking a Quantitative Analytics Tech Lead who will be responsible for the development and execution of quantitative analytics and modeling projects in support of Freddie Mac’s fair lending activities.  Apply now and learn why there’s #MoreAtFreddieMac!

Our Impact:

• Our team is responsible for fair lending testing of Freddie Mac models and systems.

• We apply modeling and statistical technologies to ensure Freddie Mac models are fair.

Your Impact:

• Lead modeling and analytical projects using techniques from statistics, machine learning and deep learning to conduct fair lending analysis of internal models and decision tools.

• Evaluate fair lending objectives on individual projects, and propose appropriate methods, techniques, and evaluation criteria for obtaining results.

• Conduct analytics and mentor junior staff in obtaining quantitative insight into fair lending questions

• Create and present materials to effectively communicate quantitative findings and fair lending implications.

• Write documentation to explain analytical decisions and comply with model oversight.

• Write programs to clean, coordinate and analyze large volumes of data.

• Make efficient use of the latest computing technologies to take advantage of big data and efficiently conduct analyses.

• Perform exploratory analysis and research and present results to both technical and non-technical audiences.

• Visualize data and analytical results to communicate complex ideas.

Qualifications:

• Doctorate degree (or Master's degree with equivalent work experience) in statistics, data science or a related quantitative field.).

• 5+ years of relevant experience applying predictive modeling techniques or data analytics to large datasets is preferred.

• 3+ years of relevant experience on leading model development, model review or fair lending projects.

• Experience applying predictive modeling techniques from data science, statistics, machine learning, and econometrics to large data sets. Qualifying coursework may include—but is not limited to—data science, statistics, machine learning, optimization, numerical analysis, scientific programming, computational methods, supervised learning, unsupervised learning, text mining, and image analysis.

• Experience writing computer programs to implement data science pipelines and predictive algorithms. Programming languages may include—but are not limited to—Python, R, SQL, Java, SAS, and MATLAB.

• Experience using technologies for manipulating structured and unstructured big data. Big data technologies may include—but are not limited to—Hadoop, Hive, Pig, Spark, relational databases, and NoSQL.

• Experience using Unix, Hadoop and related technologies (Spark, Hive, etc.) as well as statistical and machine learning libraries such as scikit-learn, MLlib and Tensorflow is preferred.

Keys to Success in this Role:

• Strong technical skills on data analytics, statistics, econometrics and data science.

• Effective interpersonal skills including the ability to collaborate successfully with other team members.

• Strong oral and written communication skills with ability to succinctly describe complex technical scenarios in simple, easily-understood terms.

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

Today, Freddie Mac makes home possible for one in four home borrowers and is one of the largest sources of financing for multifamily housing. Join our smart, creative and dedicated team and you’ll do important work for the housing finance system and make a difference in the lives of others.

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.

A safe and secure environment is critical to Freddie Mac’s business. This includes employee commitment to our acceptable use policy, applying a vigilance-first approach to work, supporting regulatory mandates, and using best practices to protect Freddie Mac from potential threats and risk. Employees exercise this responsibility by executing against policies and procedures and adhering to privacy & security obligations as required via training programs.

CA Applicants:  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.

Notice to External Search Firms: Freddie Mac partners with BountyJobs for contingency search business through outside firms. Resumes received outside the BountyJobs system will be considered unsolicited and Freddie Mac will not be obligated to pay a placement fee. If interested in learning more, please visit www.BountyJobs.com and register with our referral code: MAC.

Time-type:Full time

FLSA Status:Exempt

Freddie Mac offers a comprehensive total rewards package to include competitive compensation and market-leading benefit programs. Information on these benefit programs is available on our Careers site.

This position has an annualized market-based salary range of $143,000 - $215,000 and is eligible to participate in the annual incentive program. The final salary offered will generally fall within this range and is dependent on various factors including but not limited to the responsibilities of the position, experience, skill set, internal pay equity and other relevant qualifications of the applicant.