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
