Quantitative Analytics Tech Lead (Collateral Models)

Freddie Mac; McLean (VA); On-site; USD 144,000 - 216,000; Full time

Job summary

Freddie Mac seeks a highly skilled Quantitative Analytics Tech Lead in McLean, VA to develop collateral models for Single Family. You will combine advanced statistics, machine learning and risk analytics to assess collateral value and credit risk, partnering with users to translate models into actionable business insights.

We value leadership, independent modeling work, and a commitment to staying current with AI developments as part of a collaborative team.

Qualifications

• PhD in economics, finance, statistics, or related quantitative discipline with 3+ years' experience, or Master’s with 5+ years relevant experience.
• Demonstrated expertise in credit risk modeling (preferable collateral modeling) and ability to work with model users to translate models into business insights.
• Proficiency in programming languages such as Python, R, C++, SQL.
• Ability to perform independent model development, data processing and cleaning.
• Knowledge of AI tools and staying current with AI advancements.

Responsibilities

• Develop collateral value and risk models for Single Family properties.
• Provide modeling and analytical support to business lines or product areas.
• Solve a broad range of problems with innovative, thorough, and practicable solutions.
• Lead modeling efforts under limited direction and collaborate across teams.

Skills

Credit risk modeling

Python

R

C++

SQL

Education

PhD in economics/finance/statistics

Master’s in quantitative field

Tools

Python

R

C++

SQL

Job description

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:

Freddie Mac’s Single Family Division is currently seeking an analytical and curious model developer who has strong business knowledge in credit risk, preferably collateral.

This Quantitative Analytics Tech Lead position will include collateral model development, analysis and support potentially including some ad hoc analysis and data work in addition to developing new models and methods of evaluating collateral.

Our Impact:

We are responsible for developing Single Family collateral models that streamline the loan origination process while improving our credit risk exposure.

Your Impact:

• Developing analytical methods and models that assess the collateral value and risk of Single Family properties.
• Providing resolutions to an extensive range of complicated problems; solutions are innovative, thorough, and practicable
• Providing modeling and analytical support to a line of business or product area
• Working under limited direction, independently determining and developing approach to solutions.

Qualifications:

• PhD in economics, finance, statistics, or related quantitative discipline with 3+ years' experience, or Master’s with 5+ years relevant experience. Business experience will be weighted more than academic degrees.
• Demonstrated expertise in credit risk modeling (preferable collateral modeling), with experience working with model users to understand the model and develop new modeling and analytics methods and ideas.
• Proficiency in one or more programming languages such as Python, R, C++, SQL
• Ability to perform independent model development, data processing and cleaning
• Demonstrated knowledge of AI tools and technologies, with a strong commitment to continuous learning and staying up-to-date with advancements in artificial intelligence

Keys to Success in this Role:

• Ability to combine strong technical skills with creativity to develop new solutions.
• Ability to translate complex statistical models and methodologies into simple business terms Strong leadership, planning and communication skills
• Ability to work with and collaborate across the team and where silos exist
• Training and experience with credit risk models
• Collaborative and professional approach working in a team setting

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.

Time-type:Full timeFLSA 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 $144,000 - $216,000 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.