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Overview
At Ford, you\u2019ll work on ideas that matter, alongside passionate people who want to make a global impact. Together, we\u2019re shaping the next era of transportation\u2014grounded in purpose, driven by progress. Make your move.<\/p>\n
Job Type: Full time
Work Type: Hybrid<\/p>\n
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We made history and now we work to transform the future \u2013 for our customers, our communities and our families. You’ll see your work on the road every day, helping people move freely and pursue their dreams. At Ford, you can build more than vehicles. Come build what matters.<\/p>\n
Do you believe data tells the real story? We do! Redefining mobility requires quality data, metrics and analytics, as well as insightful interpreters and analysts. That’s where Global Data Insight & Analytics makes an impact. We advise leadership on business conditions, customer needs and the competitive landscape. With our support, key decision makers can act in meaningful, positive ways. Join us and use your data expertise and analytical skills to drive evidence-based, timely decision making.<\/p>\n
In this position…<\/p>\n
We are seeking a highly motivated and quantitatively skilled Credit Loss Model Analyst to join our Credit Risk Analytics team. This role is crucial in developing, enhancing, and maintaining the models used to forecast credit losses (Probability of Default, Loss Given Default, Exposure at Default) across our auto loan portfolio.
The successful candidate will leverage a solid quantitative background, creative problem-solving abilities, and experience with a range of modeling methodologies, including traditional statistical techniques, machine learning, and artificial intelligence, to deliver robust and insightful credit loss forecasts essential for business decision-making, capital planning, and regulatory compliance.
\ubb34\uc5c7\uc744 \ud560 \uac83\uc778\uac00...<\/p>\n
Research, develop, validate, and implement advanced statistical and quantitative models for credit loss forecasting (PD, LGD, EAD) at various levels of granularity (e.g., segment, portfolio).
Explore, evaluate, and apply innovative modeling techniques, including machine learning and artificial intelligence methods, alongside traditional econometric and statistical approaches.
Analyze complex credit performance data, portfolio characteristics, and macroeconomic trends from diverse data sources and platforms.
Enhance and maintain existing credit loss models throughout their lifecycle, ensuring performance, stability, and adherence to model governance standards.
Collaborate closely with partners in Risk Management, Finance, Accounting, and other business units to understand modeling needs, communicate model capabilities and limitations, and explain forecasting results.
Translate complex quantitative analyses, modeling methodologies, and results into clear, concise, and actionable insights for both technical and non-technical audiences, including senior leadership.
Support regulatory requirements related to credit loss forecasting, such as ECL\/CECL (Expected Credit Loss\/Current Expected Credit Loss) and stress testing, by ensuring model soundness and comprehensive documentation.
Conduct ad-hoc quantitative analysis to support portfolio performance monitoring, risk assessment, and strategic initiatives.
Develop and maintain comprehensive model documentation in accordance with internal policies and regulatory expectations.
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Master’s degree in a quantitative field such as Statistics, Economics, Mathematics, Operations Research, Data Science, Computer Science or a related discipline.
Demonstrated experience in developing and implementing statistical or quantitative models, preferably in a financial services or credit risk environment.
Strong proficiency in traditional statistical modeling techniques (e.g., linear and non-linear regression, time series analysis, panel data models, survival analysis, multivariate techniques).
Proven knowledge of Machine Learning and Artificial Intelligence concepts and techniques relevant to quantitative modeling problems (e.g., classification, regression, time series forecasting with ML\/AI methods).
Experience with statistical modeling software (e.g., SAS is highly preferred; proficiency in Python or R with relevant statistical\/ML libraries is also valuable).
Experience querying and manipulating large datasets using SQL.
Solid analytical, problem-solving, and creative thinking skills with the ability to formulate problems and propose innovative solutions.
Excellent written and verbal communication skills, with the ability to effectively translate complex quantitative concepts into business terms.
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PhD in a quantitative field such as Statistics, Economics, Mathematics, Operations Research, Data Science, Computer Science or a related discipline.
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\uc720\uae09 \ud734\uac00 \ubc0f \ucd94\uac00 \ud734\uac00 \uad6c\ub9e4 \uc635\uc158.
This position is a salary grade 6 and ranges from $85,400-$143,200.<\/p>\n
This position is a salary grade 7 and ranges from $99,600-$166,600.<\/p>\n
Final determination of salary grade will be based on candidate’s skills and experience, and base salary will be set within the applicable range according to job scope, responsibility and competitive market value.<\/p>\n
For more information on salary and benefits, click here: https:\/\/fordcareers.co\/GSR<\/p>\n
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This position is hybrid. Candidates who are in commuting distance to a Ford hub location may be required to be onsite four or more days per week. #LI-Hybrid<\/p>\n
#LI-MK2
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