Research and Development: Conducting research to identify innovative data science techniques and methodologies to solve complex problems.

Data Collection and Preprocessing: Acquiring, cleaning, and preprocessing large volumes of data from various sources to make it suitable for analysis.

Algorithm Development: Designing and implementing advanced machine learning algorithms and statistical models to extract insights from data.

Software Development: Developing scalable and efficient software systems and applications for data analysis, visualization, and deployment of machine learning models.

Database Management: Designing and optimizing databases to store and retrieve large volumes of structured and unstructured data efficiently.

Frontend Development: Building intuitive and user-friendly interfaces for data visualization and analysis, using technologies like HTML, CSS, ReactJs.

Backend Development: Developing server-side logic and APIs to support frontend applications, using frameworks like Django and Django Rest framework.

Cloud Computing: Leveraging cloud platforms like Azure data storage, computation, and deployment of machine learning models.

Data Visualization with Power BI: Creating interactive and insightful visualizations using Power BI to communicate data-driven insights effectively to stakeholders.

Version Control and Collaboration: Using tools like Git for version control and collaborating with team members on code development and experimentation.

Testing and Validation: Conducting thorough testing and validation of algorithms and software systems to ensure accuracy, reliability, and robustness.

Documentation: Documenting code, algorithms, and processes to facilitate knowledge sharing and replication of results by other team members.

Continuous Learning: Keeping up-to-date with the latest advancements in data science, machine learning, and software engineering through self-learning and attending relevant conferences and workshops.