• Extract, transform, aggregate, and analyze large and complex datasets using SQL, Python, R, Spark, and related technologies. • Perform exploratory data analysis (EDA), feature engineering, and data validation to support analytics and modeling initiatives. • Develop, evaluate, and maintain descriptive and predictive machine learning models using tools such as scikit-learn, Jupyter Notebooks, Python, R, and/or SAS. • Apply statistical modeling and data mining techniques, including regression, classification, clustering, decision trees, and related methodologies. • Utilize Generative AI and AI-assisted tools, including LLMs, coding assistants, and AutoML platforms, to improve analytical workflows and insight generation. • Apply Generative AI techniques such as prompt engineering, text summarization, classification, and LLM-assisted analysis in business or research applications. • Collaborate with stakeholders to translate business problems into analytical solutions and communicate findings effectively. • Build and maintain business intelligence solutions and dashboards using Tableau, Power BI, or similar visualization platforms. • Define metrics, validate data quality, and support semantic layer development to ensure accurate reporting and business insights. • Follow responsible AI principles, including awareness of data privacy, bias mitigation, ethical AI usage, and model limitations.

• Extract, transform, aggregate, and analyze large and complex datasets using SQL, Python, R, Spark, and related technologies. • Perform exploratory data analysis (EDA), feature engineering, and data validation to support analytics and modeling initiatives. • Develop, evaluate, and maintain descriptive and predictive machine learning models using tools such as scikit-learn, Jupyter Notebooks, Python, R, and/or SAS. • Apply statistical modeling and data mining techniques, including regression, classification, clustering, decision trees, and related methodologies. • Utilize Generative AI and AI-assisted tools, including LLMs, coding assistants, and AutoML platforms, to improve analytical workflows and insight generation. • Collaborate with stakeholders to translate business problems into analytical solutions and communicate findings effectively. • Build and maintain business intelligence solutions and dashboards using Tableau, Power BI, or similar visualization platforms. • Follow responsible AI principles, including awareness of data privacy, bias mitigation, ethical AI usage, and model limitations.

• Extract, transform, aggregate, and analyze large and complex datasets using SQL, Python, R, Spark, and related technologies. • Perform exploratory data analysis (EDA), feature engineering, and data validation to support analytics and modeling initiatives. • Develop, evaluate, and maintain descriptive and predictive machine learning models using tools such as scikit-learn, Jupyter Notebooks, Python, R, and/or SAS. • Apply statistical modeling and data mining techniques, including regression, classification, clustering, decision trees, and related methodologies. • Utilize Generative AI and AI-assisted tools, including LLMs, coding assistants, and AutoML platforms, to improve analytical workflows and insight generation. • Apply Generative AI techniques such as prompt engineering, text summarization, classification, and LLM-assisted analysis in business or research applications. • Collaborate with stakeholders to translate business problems into analytical solutions and communicate findings effectively. • Build and maintain business intelligence solutions and dashboards using Tableau, Power BI, or similar visualization platforms. • Define metrics, validate data quality, and support semantic layer development to ensure accurate reporting and business insights. • Follow responsible AI principles, including awareness of data privacy, bias mitigation, ethical AI usage, and model limitations.

• Extract, transform, aggregate, and analyze large and complex datasets using SQL, Python, R, Spark, and related technologies. • Perform exploratory data analysis (EDA), feature engineering, and data validation to support analytics and modeling initiatives. • Develop, evaluate, and maintain descriptive and predictive machine learning models using tools such as scikit-learn, Jupyter Notebooks, Python, R, and/or SAS. • Apply statistical modeling and data mining techniques, including regression, classification, clustering, decision trees, and related methodologies. • Utilize Generative AI and AI-assisted tools, including LLMs, coding assistants, and AutoML platforms, to improve analytical workflows and insight generation. • Apply Generative AI techniques such as prompt engineering, text summarization, classification, and LLM-assisted analysis in business or research applications. • Collaborate with stakeholders to translate business problems into analytical solutions and communicate findings effectively. • Build and maintain business intelligence solutions and dashboards using Tableau, Power BI, or similar visualization platforms. • Define metrics, validate data quality, and support semantic layer development to ensure accurate reporting and business insights. • Follow responsible AI principles, including awareness of data privacy, bias mitigation, ethical AI usage, and model limitations.