About The Role

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.

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.

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.