Data Scientist

Ford • Dearborn, MI

9d • $85,400 - $192,900 • Hybrid

About The Position

Creating the future of smart mobility requires the highly intelligent use of data, metrics, and analytics. That’s where you can make an impact as part of our Global Data Insight & Analytics (GDIA) team. We are the trusted advisers that enable Ford to clearly see 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.
The Marketing and Enterprise Analytics team within Global Data Insights and Analytics (GDI&A) supports analytical solutions and insights across the enterprise in areas like Corporate Finance, Office of General Counsel, General Auditors Office, Human Resources, and others. The Data Scientist discovers and curates new and existing data sources to create insight for the business. This is an exciting opportunity to utilize the latest tools and methods in Statistics, Big Data, Data Science, and Applied Generative AI to solve a variety of business problems and help build the next generation of intelligent, autonomous systems.
The role spans the full project lifecycle from analytic problem definition, through data gathering, analysis, model development, agentic workflow design, reporting/visualization development, testing, and operational deployment.
As a Data Scientist, you will join the Product Team, collaborating closely with Product Managers, Product Designers, Software Engineers and fellow Data Scientists to deliver impactful analytic solutions. In this role, you will take ownership of the full technical lifecycle, handling both the end-to-end development and the ongoing support and maintenance of these solutions.
You'll work across the full-stack technologies to enable the highest priority work to be delivered. Within this highly collaborative environment, you will:
Acquire deep understanding of the business problems and translate them into appropriate business solutions.
Drive development and delivery of analytic and statistical models using skills such as data acquisition and management, algorithm design, and model development & refinement.
Ensure overall quality of the data & solutions throughout the analytic development process; interpret results and communicate insights to technical and non-technical audiences including executive leadership.
Agent Orchestration & Cognitive Architecture: Design and program multi-step reasoning frameworks and agentic loops using modern orchestration tools (e.g., LangChain, LlamaIndex, or Haystack).
Context Engineering & Advanced RAG: Architect advanced retrieval-augmented generation (RAG) structures, utilizing embedding models, dynamic chunking strategies, and vector indices to ensure agents receive high-fidelity business context.
Applied Generative AI & SLM Strategy: Evaluate, select, and adapt Small Language Models (SLMs) (e.g., Phi, Mistral, Gemma) for domain-specific agentic tasks. Design decision frameworks for model selection based on task complexity, context requirements, latency, and inference cost.
Rigorous System Evaluation: Establish statistical, model-driven, and human-in-the-loop testing benchmarks to empirically validate agent reasoning, track accuracy drift, and minimize hallucinations.
Token Economics & Efficient Agent Design: Architect agents prioritizing token efficiency, developing strategies for context compression, prompt caching, and dynamic context windowing.

Requirements

• Master's degree in Statistics, Economics, Data Science, Computer Science, Engineering, Mathematics or relevant degree.
• 5+ years of experience in statistical or mathematical modeling, Machine Learning, Data Science or AI Engineering.
• 3+ years' experience with cloud platforms (e.g. GCP, Azure, AWS).
• 1+ years of hands-on experience designing, developing, and deploying Agentic AI solutions, including Retrieval-Augmented Generation (RAG) pipelines and LLM/SLM integrations.
• 1+ years of experience in Accounting or Finance.
• Large scale data manipulation and mining/pattern recognition experience.
• Developed and implemented Machine Learning models.
• Orchestration Mastery: Hands-on capability with modern AI orchestration frameworks, LLM APIs, and complex behavioral prompt engineering methodologies.
• SLM & Fine-Tuning Expertise: Experience with domain-specific model adaptation, including parameter-efficient fine-tuning and model distillation to embed internal business rules and vocabulary.
• Strong oral and written communication skills.
• Strong problem formulation and problem solving skills.
• Experience communicating findings to make data analysis actionable and understandable by business partners.
• Ability to take complex problems and break them down to create and implement an action plan.
• Strong understanding of analysis and computational complexity and ability to programmatically solve problems.
• Strong capabilities at designing visual interface for data interaction.

Responsibilities

• Acquire deep understanding of the business problems and translate them into appropriate business solutions.
• Drive development and delivery of analytic and statistical models using skills such as data acquisition and management, algorithm design, and model development & refinement.
• Ensure overall quality of the data & solutions throughout the analytic development process; interpret results and communicate insights to technical and non-technical audiences including executive leadership.
• Design and program multi-step reasoning frameworks and agentic loops using modern orchestration tools (e.g., LangChain, LlamaIndex, or Haystack).
• Architect advanced retrieval-augmented generation (RAG) structures, utilizing embedding models, dynamic chunking strategies, and vector indices to ensure agents receive high-fidelity business context.
• Evaluate, select, and adapt Small Language Models (SLMs) (e.g., Phi, Mistral, Gemma) for domain-specific agentic tasks. Design decision frameworks for model selection based on task complexity, context requirements, latency, and inference cost.
• Establish statistical, model-driven, and human-in-the-loop testing benchmarks to empirically validate agent reasoning, track accuracy drift, and minimize hallucinations.
• Architect agents prioritizing token efficiency, developing strategies for context compression, prompt caching, and dynamic context windowing.

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.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level