Freddie Mac
Quantitative Analytics Tech Lead (Hybrid- 3 Days in Office)
Freddie Mac, Mc Lean, Virginia, us, 22107
At Freddie Mac, you will do important work to build a better housing finance system and you’ll be part of a team helping to make homeownership and rental housing more accessible and affordable across the nation.Position Overview:
Are you curious and analytical? Do you thrive on working in a team environment? Are you looking for an inclusive company culture? Freddie Mac’s Single-Family (SF) Acquisitions Division’s Credit Analytics, Reporting and Decision Strategy (CARDS) group needs creative, forward-thinking individuals like you! We want to hire individuals who seek out complex business problems and develop viable solutions. The Quantitative Analytics Tech Lead position is responsible for ensuring the reasonableness and consistency of statistical models, analytics and methodologies used in decision making in SF guarantee business.Our Impact:
Our team is business user to a number of Tier 1 models. Our goal is to use all available models and tools to facilitate and optimize credit risk management and decision strategies.Our team is responsible for forecast SF credit loss and enhance company decision making in terms of loan acquisition.Your Impact:
Take the leading role of performing analysis to provide insights for credit risk management, supporting team in the enhancement of SF models, involving in the oversight, documentation, and the testing of new model changes and model implementation process.Actively review and ensure concise, complete, and transparent model tracking report to SF Acquisition division.Understand mortgage’s default and prepayment modeling framework and provide insights on model development methodology and modeling results.Communicate to senior management on various model and model use related topics.Respond quickly to urgent quantitative analysis requests.Qualifications:
Doctorate degree plus 3 years of working experience (or Master's degree with equivalent 5 years of working experience) in economics, statistics, data science or a related quantitative field.5+ years of relevant experience applying predictive modeling techniques from finance, statistics, mathematics, data science, and computer programming to large data sets is preferred.Experience writing statistical and/or optimization programs to develop models and algorithms. Programming languages may include—but are not limited to—Python, R, SQL, SAS, and MATLAB.Demonstrated experience and expertise in credit risk modeling and risk mitigation.Strong analytical and problem-solving skills with a high level of attention to detail.Excellent interpersonal and communication skills, particularly written communication skills.Self‐motivated, with the capability of managing multiple priorities and efficient in proposing solutions.Keys to Success in this Role:
Intellectual agility and interpersonal flexibilityAbility to work with and collaborate across the team and where silos exist.Deep curiosity to learn about the latest trends.Ability to use data to help inform strategy and direction.Problem solver, innovative and ability to think outside of the box.Ability to translate complex statistical models and methodologies into simple business terms.Strong leadership, planning and communication skills.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.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: ExemptFreddie 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.
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Are you curious and analytical? Do you thrive on working in a team environment? Are you looking for an inclusive company culture? Freddie Mac’s Single-Family (SF) Acquisitions Division’s Credit Analytics, Reporting and Decision Strategy (CARDS) group needs creative, forward-thinking individuals like you! We want to hire individuals who seek out complex business problems and develop viable solutions. The Quantitative Analytics Tech Lead position is responsible for ensuring the reasonableness and consistency of statistical models, analytics and methodologies used in decision making in SF guarantee business.Our Impact:
Our team is business user to a number of Tier 1 models. Our goal is to use all available models and tools to facilitate and optimize credit risk management and decision strategies.Our team is responsible for forecast SF credit loss and enhance company decision making in terms of loan acquisition.Your Impact:
Take the leading role of performing analysis to provide insights for credit risk management, supporting team in the enhancement of SF models, involving in the oversight, documentation, and the testing of new model changes and model implementation process.Actively review and ensure concise, complete, and transparent model tracking report to SF Acquisition division.Understand mortgage’s default and prepayment modeling framework and provide insights on model development methodology and modeling results.Communicate to senior management on various model and model use related topics.Respond quickly to urgent quantitative analysis requests.Qualifications:
Doctorate degree plus 3 years of working experience (or Master's degree with equivalent 5 years of working experience) in economics, statistics, data science or a related quantitative field.5+ years of relevant experience applying predictive modeling techniques from finance, statistics, mathematics, data science, and computer programming to large data sets is preferred.Experience writing statistical and/or optimization programs to develop models and algorithms. Programming languages may include—but are not limited to—Python, R, SQL, SAS, and MATLAB.Demonstrated experience and expertise in credit risk modeling and risk mitigation.Strong analytical and problem-solving skills with a high level of attention to detail.Excellent interpersonal and communication skills, particularly written communication skills.Self‐motivated, with the capability of managing multiple priorities and efficient in proposing solutions.Keys to Success in this Role:
Intellectual agility and interpersonal flexibilityAbility to work with and collaborate across the team and where silos exist.Deep curiosity to learn about the latest trends.Ability to use data to help inform strategy and direction.Problem solver, innovative and ability to think outside of the box.Ability to translate complex statistical models and methodologies into simple business terms.Strong leadership, planning and communication skills.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.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: ExemptFreddie 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.
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