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 Credit Analytics, Reporting and Decision Strategy 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 the SF guarantee business.

Our Impact:

Our team is the business driver to a number of tier 1 models and processes. Our goal is to utilize all available models and tools to facilitate and optimize SF acquisition and profitability strategies.

This is an increasingly critical role given the current mortgage and economic environment.

Your Impact:

• Leading a cooperative effort among members of the team and collaborate with external stakeholders to provides timely, relevant, robust analyses to support credit and strategic decisions
• Initiating and designing appropriate analytical approaches for a variety of business questions or scenarios presented regarding credit and regulatory capital.
• Guiding the robust developments on frameworks and tools that enable relatively quickly, deep understanding of relevant data, business background for managements and downstream users
• Developing strategies to analyze and interpret output of models or analytic applications, with emphasis on understanding and evaluating the model structure, model drivers, and model performance. Plan, execute, and document analysis of complex financial models
• Guiding the appropriate modeling development in loan performance transition.
• Participating in the development of an analytical framework for new initiatives
• Building strong productive relationship with various stakeholders across 3 line of defenses

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.
• 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 analytics skills with investigation attention to detail.
• Excellent presentation on complex concepts through visualization
• Self-motivated with minimum supervision, with the capability of managing multiple priorities and efficient in proposing solutions

Keys to Success in this Role:

• Intellectual agility and interpersonal flexibility
• 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
• Deep curiosity to learn about new trends Ability to utilize data to help inform strategy and direction
• Solid aptitude for solving problems, innovative and out of box problem solver

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

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.