Data scientist

• Provide technical leadership across Python, Data Engineering, Data Science, and Machine Learning initiatives. • Translate business and functional requirements into scalable technical solutions. • Define the overall solution design, technical architecture, integration patterns, and implementation approach. • Design and develop robust, scalable, reusable Python applications and data processing frameworks. • Develop modular and maintainable Python code following software engineering best practices. • Build reusable libraries, utilities, components, and frameworks for Data Science and ML applications. • Perform data manipulation and large-scale data processing using libraries such as Pandas, NumPy ,PySpark. • Build integration components using REST APIs, JSON, files, databases, and cloud services. • Write efficient Python code for processing large datasets. • Perform code reviews, debugging, performance optimization, and production troubleshooting. • Python libraries such as Pandas, NumPy and Scikit-learn remain foundational technologies for data manipulation and ML development. • Review exploratory data analysis, feature engineering, model selection, training, validation, and evaluation approaches. • Provide technical guidance for: o Classification o Regression o Clustering o Forecasting o Anomaly detection o Predictive analytics • Build automation frameworks and utilities for data engineering processes. • Lead and manage offshore data engineering teams. Collaborate with business users, architects, and project managers.