Analytics Scientist – Dearborn, MI (Hybrid) | Visa + Salary Insights

📍 Location: us

🏷 Type: Not specified

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Job Overview

This role is for a highly quantitative Analytics Scientist focused on credit risk modeling within a major global automotive finance ecosystem at Ford Motor Company. The position sits inside Global Data Insight & Analytics and centers on developing advanced credit loss forecasting models including PD, LGD, and EAD. The ideal candidate is a strong statistician or data scientist with experience in financial modeling, machine learning, and large-scale data systems. This is a hybrid role requiring collaboration with finance, risk, and executive stakeholders to drive regulatory compliance and strategic decision-making.

📅 Job Timeline & Status

This is in an early-to-mid hiring cycle, with a very short application window indicating structured intake and potentially high competition. Given the tight deadline and enterprise-level scope, candidates should apply immediately. Roles of this nature typically move fast due to regulatory and forecasting needs tied to credit risk operations.

🌍 Work Eligibility & Location

This role is primarily US-based with strict work authorization requirements, but visa sponsorship is explicitly available, making it accessible for strong international quantitative talent. However, hybrid onsite expectations significantly reduce full-remote flexibility.

💰 Salary Intelligence

This is a highly competitive compensation structure for a credit risk analytics role. The upper band aligns with senior-level quantitative modelers with production-grade experience in regulatory frameworks. Total compensation value increases significantly with bonus eligibility and enterprise benefits.

📊 Role Breakdown

This position focuses on building and maintaining advanced credit risk models that directly influence lending decisions, capital planning, and regulatory reporting. You will work with large-scale financial datasets, combining structured credit performance data with macroeconomic indicators. A major part of the role involves designing Probability of Default (PD), Loss Given Default (LGD), and Exposure at Default (EAD) models using both classical statistical techniques and modern machine learning pipelines.

Expect to work heavily in Python, SAS, and SQL for data extraction, transformation, and modeling workflows. Time-series forecasting, survival analysis, and multivariate regression are core techniques used to evaluate portfolio behavior over time. Approximately 40–50% of the role involves model development, while another 30% focuses on validation, monitoring, and governance compliance under regulatory frameworks like CECL.

The remaining portion involves stakeholder communication—translating complex quantitative outputs into actionable business insights for finance leadership. You will also support stress testing scenarios, portfolio risk simulations, and macroeconomic sensitivity analysis.

🧩 Required Skills & Fit

📈 Difficulty & Competitiveness

This is a technically demanding role requiring strong mathematical foundations and applied financial modeling expertise. Competition is intense due to the combination of strong brand value, visa sponsorship, and high compensation ceiling.

🚀 Career Impact

This role significantly strengthens expertise in regulated AI systems, credit modeling, and enterprise-scale analytics. It positions candidates for future leadership roles in risk analytics, financial AI systems, and quantitative strategy domains.

📋 Key Responsibilities

You will design, validate, and deploy advanced statistical models supporting enterprise credit forecasting. Core responsibilities include building PD, LGD, and EAD models using Python, SAS, and machine learning frameworks. You will analyze large-scale credit datasets, integrate macroeconomic variables, and perform scenario-based stress testing.

A critical function is maintaining model governance compliance under CECL regulatory requirements, ensuring models are both explainable and auditable. You will also collaborate with cross-functional teams including risk, accounting, and finance to translate quantitative outputs into business decisions.

Additional duties include portfolio monitoring, ad-hoc risk analysis, and continuous model improvement using performance diagnostics and back-testing methodologies.

🎯 Application Strategy

🧠 Application Optimization (Adaptive)

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🧠 Fit & Positioning Analysis

Evaluate your alignment carefully before applying.

📅 Application Signals

This role shows high urgency due to a short application window and enterprise-level risk requirements. Competition is expected to be strong, especially from candidates in quantitative finance and AI-driven analytics backgrounds. Because the deadline is tight and structured, interview cycles are likely fast-moving.

Urgency is high, competition is global, and shortlisted candidates may be contacted quickly within days of submission. If applying, immediate action is strongly recommended. Applications may close without notice if sufficient candidate volume is reached.

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✅ Job Source & Verification

This listing is based on an official posting from Ford Motor Company careers portal, a globally recognized employer in automotive and financial services analytics. Data was last reviewed on 12 May 2026, with posting verified against the original job description. All salary, role scope, and eligibility details align with enterprise-grade credit risk analytics hiring standards.