SUMMARY:
ESSENTIAL SKILLS:

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

• MLOps practices (model packaging, deployment pipelines, monitoring awareness)
• Data governance princ...