Pros
• Supportive management that encourages learning and experimentation.
• Got hands-on exposure to real datasets and complete end-to-end data science projects.
• Learned practical skills in Python, SQL, Power BI, and machine learning model deployment.
• Collaborative and flexible work environment with a focus on innovation.
• Great place to build a strong foundation if you’re early in your data career.
Cons
• Workload can be high during project deadlines.
• Need more cross-team communication between engineering and analytics teams.