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Quantitative Analytics Tech Lead – Model Validation

Freddie Mac -
McLean, VA

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Job Details

Full-time
$144,000 - $216,000 a year

Qualifications

Math
Data-driven problem-solving

Full 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 Enterprise Risk Division is currently seeking a Quantitative Analytics Teach Lead to join the Enterprise Model Risk Group. The Enterprise Model Risk Group is responsible for model risk oversight including development, validation, implementation and monitoring models across multiple risk areas. The team is primarily responsible for validating key models used by the Single-Family Business Division. The models subject to review include origination scorecard model, Automated Valuation model, and credit loss models.

Our Impact:

Freddie Mac is leading the U.S housing market forward by making homeownership and rental housing more accessible and affordable.

Your Impact:

Perform comprehensive validation of Single-Family Models in accordance with applicable Freddie Mac Policy and Standards and Regulatory requirements.
Review appropriate controls for associated model overlays, processes and applications that may include expert judgment or qualitative factors.
Recommend, as appropriate, the approval or disapproval of models, overlays, processes, and applications, and raise findings tied to weaknesses as determined by independent effective challenge.
Ensure conformance to internal control standards for associated model validation processes.
Interact effectively with model developers, implementers and users, business stakeholders, and governance committees to manage model validations, including operational management of associated model risk processes, and ensure compliance with Model Risk Policy and Standards
Participate in and support audit and regulatory engagements, including remediation activities related to model risk.
Conduct ongoing monitoring and validation activities to support conformance to appropriate risk appetite and KRI metrics.
Engage with model governance and reporting functions, and other model validation groups, and support model risk activities.
Build strong relationships and consensus across the lines of defense on model validation matters, aligning conflicting priorities and resolving issues to achieve progress.

Qualifications:

PhD in economics, finance, statistics, or related quantitative discipline with 3+ years' experience, or Master’s with 5+ years relevant experience.
Coursework or work experience applying predictive modeling techniques from finance, statistics, mathematics, data science, and computer programming to large datasets. Qualifying coursework may include statistics, mathematical programming, optimization, machine learning, computational methods, design and analysis of algorithms, Bayesian methods, derivatives, and Monte Carlo methods/modeling.
Experience in regulated financial services institutions preferred.

Keys to Success in this Role:

Deep intellectual curiosity to explore new methods and acquire techniques beyond one’s own expertise.
Ability to translate analytical results into actionable insights that inform strategy and business direction.
Proficiency in machine learning and AI techniques.
Strong technical and programming skills for processing large datasets and building/validating complex models.
Superior technical writing and verbal communication skills.
Excellent organizational skills and ability to manage multiple priorities

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 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 $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.

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