Senior Data Scientist – Go-to-Market Advanced Analytics; Yield Management

Our Marketing, Sales & Service organization advances the Ford reputation as a visionary vehicle and mobility services company and helps deliver a trusted customer experience. Use your marketing, sales and service expertise to turn data-driven insights into innovative solutions that enhance sales and customer loyalty. Join us and be the eyes, ears and voice of Ford.

In this position...
This role sits within the Yield Management Advanced Analytics team, focused on developing and maintaining sophisticated modeling and optimization capabilities that drive pricing and incentive strategy across the business. As part of the broader Yield Management organization, this team partners closely with sibling analytics products, data engineering, AI/ML engineering, and business stakeholders to deliver quantitative solutions that shape how the company prices vehicles, structures incentives, and manages portfolio risk.

The successful candidate will serve as a subject matter expert on our analytical products, translating complex business problems into rigorous quantitative solutions and owning their full lifecycle - from framework design through production deployment and ongoing maintenance. Solutions are delivered in whatever format best fits the need, often as always-on web applications (e.g., Python Dash apps), but also as ad-hoc analyses, or dashboards when appropriate.

This position requires strong technical depth in statistical modeling, machine learning, and optimization, along with sound judgment in applying these methods to real business constraints. There is also room to explore emerging tools - such as agentic AI and LLM-based workflows - to enhance how solutions are built and delivered.

• Assessing portfolio risk and opportunity at the portfolio or nameplate level
• Assessing business impacts of different incentive tactics to support incentive planning
• Optimizing nameplate profitability or minimizing risk by adjusting entity-level pricing based on production, market demand, and operational constraints
• Supporting new model year launches, mid-year repricing, and short-term variable marketing adjustments

• Own the end-to-end lifecycle of complex predictive models and optimization frameworks, driving scheduled updates, continuous improvement, and maintenance using advanced statistical and machine learning methodologies
• Serve as subject matter expert for our analytical products, ensuring the accuracy of underlying data, calculation logic, and business assumptions
• Drive the transition of analytical frameworks into production-ready assets, focusing on deployment automation, operational stability, and delivering clean, modular, well-documented code
• Explore opportunities to apply emerging techniques, such as agentic AI or LLM-based tools, to improve modeling workflows or analytical delivery

• Partner directly with business stakeholders to understand evolving needs and convert ambiguous business problems into well-defined analytical problems
• Communicate quantitative concepts into clear, actionable insights for both technical peers and business partners, ensuring analytical work translates into real business impact
• Execute rapid-response, ad-hoc analytical requests to support time-sensitive strategic decision-making
• Partner closely with data engineering, AI/ML engineering, and other analytics product teams within Yield Management to align data science capabilities with evolving business needs

• Master's degree in Data Science, Statistics, Economics/Econometrics, Mathematics, Operations Research, Artificial Intelligence, Machine Learning, or a related quantitative field
• 3+ years of experience in data analytics, AI/ML modeling, or related quantitative analysis
• Proficiency in one or more programming languages (Python, R, MATLAB, C/C++/C#, SAS), along with familiarity in ML/DL libraries and SQL

• Ph.D. in Data Science, Statistics, Economics/Econometrics, Mathematics, Operations Research, Artificial Intelligence, Machine Learning, or a related quantitative field
• Demonstrated skills applying econometrics, statistics, and/or machine/deep learning techniques to large, structured and unstructured data sets
• Strong problem formulation and analytical rigor, with the ability to interpret and communicate model results
• Experience with web app frameworks (e.g., Dash, Flask,…