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.