Ford/GDIA Mission and Scope:

At Ford Motor Company, we believe freedom of movement drives human progress. We also believe in providing you with the freedom to define and realize your dreams. With our incredible plans for the future of mobility, we have a wide variety of opportunities for you to accelerate your career potential as you help us define tomorrow’s transportation.

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 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 Global Data Insights and Analytics (GDI&A) department at Ford Motors Company is looking for qualified people who can develop scalable solutions to complex real-world problems using Machine Learning, Big Data, Statistics, Econometrics, and Optimization. The goal of GDI&A is to drive evidence-based decision making by providing insights from data. Applications for GDI&A include, but are not limited to, Connected Vehicle, Smart Mobility, Advanced Operations, Manufacturing, Supply chain, Logistics, and Warranty Analytics.

About the Role:

You will be part of the FCSD analytics team, playing a critical role in leveraging data science to drive significant business impact within Ford Customer Service Division. As a Data Scientist, you will translate complex business challenges into data-driven solutions. This involves partnering closely with stakeholders to understand problems, working with diverse data sources (including within GCP), developing and deploying scalable AI/ML models, and communicating actionable insights that deliver measurable results for Ford.

Qualifications:

At least 3 years of relevant professional experience applying data science techniques to solve business problems. This includes demonstrated hands-on proficiency with SQL and Python. Bachelor's or Master's degree in a quantitative field (e.g., Statistics, Computer Science, Mathematics, Engineering, Economics). Hands-on experience in conducting statistical data analysis (EDA, forecasting, clustering, hypothesis testing, etc.) and applying machine learning techniques (Classification/Regression, NLP, time-series analysis, etc.).  

Technical Skills:

Proficiency in SQL, including the ability to write and optimize queries for data extraction and analysis. Proficiency in Python for data manipulation (Pandas, NumPy), statistical analysis, and implementing Machine Learning models (Scikit-learn, TensorFlow, PyTorch, etc.). Working knowledge in a Cloud environment (GCP, AWS, or Azure) is preferred for developing and deploying models. Experience with version control systems, particularly Git. Nice to have: Exposure to Generative AI / Large Language Models (LLMs).  

Functional Skills:

Proven ability to understand and formulate business problem statements. Ability to translate Business Problem statements into data science problems. Strong problem-solving ability, with the capacity to analyze complex issues and develop effective solutions. Excellent verbal and written communication skills, with a demonstrated ability to translate complex technical information and results into simple, understandable language for non-technical audiences. Strong business engagement skills, including the ability to build relationships, collaborate effectively with stakeholders, and contribute to data-driven decision-making.

 

Job Responsibilities:

Build an in-depth understanding of the business domain and data sources, demonstrating strong business acumen. Extract, analyze, and transform data using SQL for insights. Apply statistical methods and develop ML models to solve business problems. Design and implement analytical solutions, contributing to their deployment, ideally leveraging Cloud environments. Work closely and collaboratively with Product Owners, Product Managers, Software Engineers, and Data Engineers within an agile development environment. Integrate and operationalize ML models for real-world impact. Monitor the performance and impact of deployed models, iterating as needed. Present findings and recommendations effectively to both technical and non-technical audiences to inform and drive business decisions.