Collect, clean, and analyze structured and unstructured data using statistical methods and machine-learning techniques. Develop, train, validate, and deploy

predictive models and algorithms using tools such as Python, R, SQL, and machine-learning frameworks (e.g., TensorFlow, Py Torch, Scikit-learn). Perform exploratory data analysis to

identify trends, patterns, and insights that support business decision-making. Design and implement data pipelines and workflows for data ingestion, transformation, and model deployment.

Build dashboards, data visualizations, and reports using tools such as Tableau, Power BI, or similar platforms. Collaborate with cross-functional teams—including engineering, product, and

business stakeholders—to define requirements and translate business problems into data-driven solutions. Validate data quality, perform feature engineering, and optimize model

performance through testing and tuning. Communicate analytical findings, methodologies, and recommendations to technical and non-technical audiences. Stay current with advancements

in data science, AI/ML methodologies, and relevant technologies.