Job Description

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 Quality Analytics.



Bachelor's degree in computer science, Operational research, Statistics, Applied mathematics, or in any other engineering discipline.

2+ years of experience developing interactive dashboards and reports using Power BI, demonstrating strong proficiency in data visualization and business intelligence.

2+ years of hands-on experience in Python programming for data analysis, machine learning, and with libraries such as NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow, PyTorch, NLTK, spaCy, and Gensim. 2+ years of experience with both supervised and unsupervised machine learning techniques. 2+ years of experience with data analysis and visualization using Python packages such as Pandas, NumPy, Matplotlib, Seaborn, or data visualization tools like Dash or QlikSense. 1+ years' experience in SQL programming language and relational databases.

Understand business requirements and analyze datasets to determine suitable approaches to meet analytic business needs and support data-driven decision-making Design and implement data analysis and ML models, hypotheses, algorithms and experiments to support data-driven decision-making

Develop and maintain dynamic dashboards and intuitive user interfaces using Power BI, visualizing complex datasets stored in Google Cloud Platform (GCP) to communicate key insights and drive data-driven decision-making.

Apply various analytics techniques like data mining, predictive modeling, prescriptive modeling, math, statistics, advanced analytics, machine learning models and algorithms, etc.; to analyze data and uncover meaningful patterns, relationships, and trends Design efficient data loading, data augmentation and data analysis techniques to enhance the accuracy and robustness of data science and machine learning models, including scalable models suitable for automation * Research, study and stay updated in the domain of data science, machine learning, analytics tools and techniques etc.; and continuously identify avenues for enhancing analysis efficiency, accuracy and robustness