Analyze business requirements and translate them into analytical problems to support data-informed decisions across enterprise operations.
Design, build, and validate machine learning models using supervised and unsupervised learning techniques.
Apply statistical analysis, predictive modeling, prescriptive analytics, and data mining techniques to identify patterns, trends, and insights.
Develop scalable data science pipelines, including data loading, data augmentation, feature engineering, and model training workflows.
Perform data analysis and visualization using Python libraries and analytical tools to effectively communicate insights to stakeholders.
Work with large datasets from domains such as mobility, manufacturing, logistics, supply chain, and operational analytics.
Collaborate with cross-functional teams and business stakeholders to implement analytical solutions and evaluate model performance.
Deploy and manage machine learning solutions on cloud environments including Google Cloud Platform.
Stay updated with emerging research, machine learning algorithms, large language models (LLMs), and advanced analytics techniques to improve solution accuracy and efficiency.
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