Pros
In my experience:
• Excellent learning environment, especially for entry-level positions
• An emphasis on research, documentation, understanding, and knowledge sharing
• Supportive and respectful team leads
• Open, inclusive, and proactive discussions about company culture and processes
• Genuinely fun team, even in stressful situations
• Respect of boundaries and work-like balance
• Effective collaboration between product and data science teams
• The product vision and company objectives take into consideration the entire team’s contributions
• Generous learning budget after probation period
• Frequent and fun team gatherings (e.g. Wednesday breakfasts, Friday evening hangouts, team events)
• Flexibility with working hours and a reasonable degree of remote work
• Conflicts are taken seriously and managed respectfully, with people given the benefit of the doubt
• Continuous giving & seeking of feedback, and of opportunities for change and improvement
• Genuine and lengthy efforts made to give everyone as many chances as possible, and to maintain a respectful relationship and process
Cons
In my experience:
• Sometimes there is a blurred division of responsibility and ownership when it comes to certain technical components, leading to problems that could easily be resolved with better communication between teams and a shared understanding of processes — but this is seeing improvement as we continuously and proactively improve said processes
• Due to different levels of understanding of data science topics, there is sometimes a lack of consensus across teams regarding the feasibility and merit of different features and approaches. This is being addressed with ongoing discussions with the product team, as well as company-wide knowledge sharing sessions