Design and develop scalable data pipelines for ingesting, processing, and storing large datasets using technologies like Spark and Kafka.

Implement machine learning models and algorithms using Python and relevant libraries (e.g., TensorFlow, PyTorch, scikit-learn) for predictive analytics.

Collaborate with data scientists to deploy and maintain machine learning models in production environments, ensuring reliability and performance.

Write clean, efficient, and well-documented code for data processing, model training, and deployment, adhering to software engineering best practices.

Optimize data storage and retrieval processes using databases (SQL and NoSQL) and cloud-based solutions (e.g., AWS, Azure, GCP) for efficient data access.

Develop and maintain APIs for accessing data and machine learning models, ensuring secure and scalable data delivery to various applications.

Conduct code reviews and participate in architectural discussions to ensure code quality, maintainability, and scalability of data science projects.

Monitor and troubleshoot data pipelines and machine learning models in production, identifying and resolving issues to ensure continuous operation.

Implement data quality checks and validation procedures to ensure data accuracy and reliability throughout the data lifecycle.

Automate data processing and model deployment tasks using scripting languages (e.g., Python, Bash) and automation tools (e.g., Jenkins, Docker).

Stay updated with the latest advancements in data science, machine learning, and software engineering, and apply them to improve existing systems.

Collaborate with cross-functional teams (e.g., product managers, data analysts) to understand requirements and deliver data-driven solutions.

Crafting efficient algorithms for data processing and machine learning models.

Writing clean, scalable code to implement data science solutions.

Building robust data pipelines for data extraction, transformation, and loading (ETL).

Deploying machine learning models to production environments.

Optimizing software and model performance for speed and efficiency.

Integrating data science components with existing software systems.

Thoroughly testing and debugging code to ensure reliability.

Collaborating with data scientists and engineers on projects.

Creating clear documentation for code and data science processes.

Participating in code reviews to maintain code quality.

Managing code using version control systems like Git.

Managing the infrastructure required for data science projects.