Data Analysis: Proficient in data manipulation, cleaning, exploration, and visualization using tools like Python (pandas, numpy, matplotlib, seaborn) or Tableau. Machine Learning Modeling: Strong understanding and practical experience with various ML algorithms, particularly time series analysis techniques (ARIMA, Prophet, LSTM, etc.). Feature Engineering: Ability to identify, create, and select relevant features to improve model accuracy and interpretability. Large-Scale Time Series Experience: Preferred experience working with large time series datasets, including data management and efficient processing techniques. Programming Proficiency: Strong programming skills in Python, including experience with relevant libraries for data science and machine learning. Statistical Knowledge: Solid foundation in statistical concepts, including hypothesis testing, regression analysis, and time series analysis.

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