Requirements

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  Data analysis, exploration, and feature engineering (EDA)
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  Strong applied statistics and machine learning foundations
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  Python-based data science and ML stack (e.g. pandas, NumPy, scikit-learn, PyTorch / TensorFlow)
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  Data engineering skills: ETL design, batch and streaming data processing
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  Experience with distributed data systems (e.g. Kafka, Spark or equivalent)
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  SQL and structured / semi-structured data querying
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  Experiment design, model evaluation, and validation techniques
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  Dashboarding, reporting, and data visualization for insights and decision support
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  Business problem translation and requirements understanding
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  Version control and collaborative development (Git)