Responsibilities:
- Develop and maintain complex data-driven software systems using Python.
- Write reusable, testable, and efficient code.
- Design and implement low-latency, high-availability, and performant data applications.
- Work with large datasets to extract, transform, and analyze data to drive business insights.
- Develop and deploy machine learning models and algorithms.
- Implement data security and data protection solutions.
- Optimize data processing pipelines and machine learning models for maximum speed and scalability.
- Collaborate with data scientists, data engineers, and other stakeholders to understand data requirements.
- Participate in code reviews and mentor junior developers and data scientists.
- Troubleshoot, debug, and upgrade existing data systems.
- Stay up-to-date with new technology trends and data science techniques.
Requirements & Qualifications:
- Educational Background: Bachelor's degree in Data Science, Computer Science, Statistics, Information Technology, or a related field.
- Strong proficiency in Python and its data science libraries (e.g., Pandas, NumPy, SciPy, Scikit-learn).
- Experience with data visualization tools (e.g., Matplotlib, Seaborn, Plotly).
- Knowledge of user authentication and authorization between multiple systems, servers, and environments.
- Understanding of fundamental design principles behind scalable data applications.
- Familiarity with event-driven programming in Python.
- Experience with SQL and NoSQL databases, data warehousing, and ETL processes.
- Proficient understanding of code versioning tools such as Git.
- Analytical Skills: Strong analytical and problem-solving skills with a keen eye for detail.
- Communication Skills: Excellent verbal and written communication skills to effectively convey complex data insights to non-technical stakeholders.
- Collaboration: Ability to work independently and collaboratively in a team environment
