Company Name

Ford ( Chennai )

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.

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