• Design, develop, validate, and deploy machine learning and AI solutions for business-critical applications. • Build scalable predictive models, anomaly detection systems, forecasting solutions, recommendation systems, and generative AI applications. • Develop conversational AI and agent-assist solutions leveraging LLMs, NLP, and retrieval-augmented generation (RAG) techniques. • Create intelligent AI agents for business workflow automation and SDLC acceleration initiatives. • Develop and optimize fraud detection models using supervised and unsupervised machine learning techniques. • Analyze structured and unstructured datasets to identify trends, patterns, risks, and business opportunities. • Partner with engineering teams to productionize AI/ML solutions and integrate them into enterprise applications and workflows. • Develop reusable ML pipelines, feature engineering frameworks, and model monitoring capabilities. • Monitor model performance, drift, reliability, and operational effectiveness in production environments. • Collaborate with product managers, engineers, business stakeholders, and risk/compliance teams to define requirements, success metrics, and implementation strategies. • Translate technical insights and analytical findings into clear business recommendations and executive-level communications. • Ensure AI and machine learning solutions comply with data governance, privacy, security, and regulatory standards. • Develop documentation supporting model explainability, validation, monitoring, and audit readiness. • Promote responsible AI practices, including fairness, transparency, and risk mitigation. • Mentor junior team members and contribute to technical standards, best practices, and continuous improvement initiatives.

• Innovate with State-of-the-Art AI: Implement cutting-edge AI solutions for key Document Understanding tasks such as OCR/HTR, transcription, Named Entity Recognition (NER), Relation Extraction (RE), Coreference Resolution, Summarization, and Knowledge Graphs working with diverse genealogical and historical collections spanning newspapers, city directories, family history books, and vital records (i.e., birth, marriage, & death records). • Analyze and Optimize Multi-Modal Models: Evaluate the performance of multi-modal models in zero-shot and few-shot learning scenarios for comprehensive document understanding. • Architect Agentic Systems: Design and implement multi-agent workflows using frameworks like LangChain, LangGraph, CrewAI, or AutoGen to automate complex multi-step reasoning tasks in historical document analysis. • Evaluation & Observability: Establish "LLM-as-a-Judge" frameworks and use tools like Arize Phoenix, DeepEval, or RAGAS to monitor for hallucination, drift, and bias. • Collaborate on Cloud Deployment: Partner closely with ML Ops and Data Science Engineers to seamlessly deploy datasets, models, and pipelines in cloud environments. • Communicate Insights Effectively: Clearly and confidently present your findings, deliverables, and proposed solutions to technical and non-technical audiences, including teams, stakeholders, and executives.

Role Description The Senior Data Scientist on the Credit AI team at Ford Credit will lead the development and deployment of advanced AI and machine learning solutions that improve customer experience, reduce risk, and drive operational efficiency. This role focuses on delivering scalable, production-ready solutions across conversational AI, fraud detection, forecasting, and intelligent automation initiatives while partnering closely with engineering, product, and business stakeholders. As a Senior Data Scientist within the Credit AI organization, you will play a critical role in shaping and delivering AI-driven solutions that support strategic business priorities across Ford Credit. You will work across a diverse portfolio of initiatives, including: - Conversational AI solutions for customer representatives - Fraud detection and risk analytics - Forecasting and predictive modeling - AI agents that automate business workflows and accelerate software development processes This role requires strong expertise in machine learning, statistical modeling, generative AI, and production AI systems. You will collaborate with cross-functional teams to: - Translate business challenges into scalable technical solutions - Develop and validate models - Ensure successful deployment into production environments - Establish best practices around model governance, monitoring, explainability, and responsible AI Success in this role will be measured through measurable business outcomes such as: - Reduced fraud losses - Improved forecast accuracy - Enhanced customer support efficiency - Increased automation effectiveness What you'll do: - Design, develop, validate, and deploy machine learning and AI solutions for business-critical applications - Build scalable predictive models, anomaly detection systems, forecasting solutions, recommendation systems, and generative AI applications - Develop conversational AI and agent-assist solutions leveraging LLMs, NLP, and retrieval-augmented generation (RAG) techniques - Create intelligent AI agents for business workflow automation and SDLC acceleration initiatives - Develop and optimize fraud detection models using supervised and unsupervised machine learning techniques - Analyze structured and unstructured datasets to identify trends, patterns, risks, and business opportunities - Partner with engineering teams to productionize AI/ML solutions and integrate them into enterprise applications and workflows - Develop reusable ML pipelines, feature engineering frameworks, and model monitoring capabilities - Monitor model performance, drift, reliability, and operational effectiveness in production environments - Collaborate with product managers, engineers, business stakeholders, and risk/compliance teams to define requirements, success metrics, and implementation strategies - Translate technical insights and analytical findings into clear business recommendations and executive-level communications - Ensure AI and machine learning solutions comply with data governance, privacy, security, and regulatory standards - Develop documentation supporting model explainability, validation, monitoring, and audit readiness - Promote responsible AI practices, including fairness, transparency, and risk mitigation - Mentor junior team members and contribute to technical standards, best practices, and continuous improvement initiatives Qualifications - Bachelor’s degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related quantitative field - 5+ years of experience developing and deploying machine learning or AI solutions in production environments - Strong programming experience in Python and experience with ML frameworks such as scikit-learn, PyTorch, TensorFlow, or similar - Experience building predictive models, forecasting solutions, anomaly detection systems, NLP applications, or generative AI solutions - Experience with large language models (LLMs), prompt engineering, retrieval-augmented generation (RAG), or conversational AI systems - Strong SQL and data manipulation skills with experience working on large-scale datasets - Experience with cloud platforms such as AWS, Azure, or GCP - Understanding of MLOps concepts including model deployment, monitoring, versioning, and CI/CD workflows - Strong analytical, problem-solving, communication, and stakeholder management skills Requirements - Master’s degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related quantitative field (preferred) - Experience in financial services, credit risk, fraud analytics, or regulated industries (preferred) - Experience with AI agents, orchestration frameworks, or automation platforms (preferred) - Experience with model explainability and governance tools such as SHAP or LIME (preferred) - Knowledge of software engineering workflows and developer productivity tooling (preferred) - Experience mentoring or leading technical teams (preferred) Benefits - Immediate medical, dental, vision and prescription drug coverage - Flexible family care days, paid parental leave, new parent ramp-up programs, subsidized back-up child care and more - Family building benefits including adoption and surrogacy expense reimbursement, fertility treatments, and more - Vehicle discount program for employees and family members and management leases - Tuition assistance - Established and active employee resource groups - Paid time off for individual and team community service - A generous schedule of paid holidays, including the week between Christmas and New Year’s Day - Paid time off and the option to purchase additional vacation time

• Design and build metrics, experiments, projections, and predictive models • Write clean, efficient code in Python and SQL • Collaborate with business stakeholders to propose data-driven solutions • Test and refine data models and solutions