Data scientist

JD

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.