
Quantitative Risk Analytics Tech Lead (Mortgage Credit Risk)
Required skills
Python
SQL
Tableau
Machine Learning
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:
Come join a growing team of strategists and problem solvers in Freddie Mac’s Single-Family Acquisitions SF Business Analytics (SFBA) team.
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Analytics, reporting, and the identification of emerging risks from our newly purchased loan portfolio are critical to Freddie Mac’s success and overall risk mitigation strategies
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Bring together business partners and identify emerging industry trends in a fast-paced, exciting and diverse work environment
Our Impact:
Our division is responsible for keeping our finger on the pulse of the mortgages we buy and their performance, ensuring that they remain within the firm’s risk appetite
Your Impact:
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Enhance and implement comprehensive analytical frameworks to measure credit risk and performance of new or existing policies/products/programs by using data and business expertise
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Provide analytical insights by leveraging machine learning, GenAI, and/or other related quantitative skills
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Automate processes to monitor emerging risk effectively and efficiently
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Develop subject matter expertise knowledge about Freddie Mac Single-Family data and the Seller/Servicer Guide and independently extract data to support analytics
Qualifications:
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Bachelor's Degree in Quantitative Finance, Statistics, Mathematics, Economics, or a related quantitative field (Master’s degree preferred) and 8-10+ years of relevant experience
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8-10+ years of experience applying analytical skills to solve business problems with large data sets
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Experience writing statistical and/or optimization programs to develop models, algorithms and/or to conduct data analytics on large data sets
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Proficiency in programming languages such as SQL (required), Python (preferred) and/or SAS
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Solid understanding of mortgage related risk, credit, and underwriting/origination life cycle.
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Excellent quantitative, analytical, and problem-solving skills
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Strong experience with data visualization tools including Tableau
Keys to Success in this Role:
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Excellent and advanced quantitative, analytical, problem-solving, critical thinking and communication skills
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Excellent presentation skills both verbal and written
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Deep curiosity about new trends and topics in mortgage products and credit risk
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Proven ability to analyze risk using quantitative and qualitative information, bridging together data insights and business questions effectively
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Advanced technical and data manipulation skills
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Ability to explore and leverage new technical skills and data sets as needed
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 applicable federal, state or local laws. We will ensure that individuals 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 Bounty Jobs for contingency search business through outside firms. Resumes received outside the Bounty Jobs 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.Bounty Jobs.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 $142,000 - $214,000 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.
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About Freddie Mac

Freddie Mac
PublicFreddie Mac is a government-sponsored enterprise that purchases mortgages from lenders and securitizes them into mortgage-backed securities. The company provides liquidity to the U.S. residential mortgage market and supports homeownership and rental housing.
5,001-10,000
Employees
McLean
Headquarters
Reviews
10 reviews
3.7
10 reviews
Work-life balance
4.0
Compensation
2.5
Culture
4.2
Career
3.0
Management
4.0
68%
Recommend to a friend
Pros
Supportive management
Good benefits and job security
Collaborative team environment
Cons
Low/uncompetitive compensation
Limited advancement opportunities
Heavy workload and long hours
Interview experience
5 interviews
Difficulty
3.2
/ 5
Interview process
1
Application Review
2
Recruiter Screen
3
Technical Phone Screen
4
Onsite/Virtual Interviews
5
Team Matching
6
Offer
Common questions
Coding/Algorithm
Technical Knowledge
Behavioral/STAR
Past Experience
Culture Fit
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