Pros
– Culturally inclusive and polite colleagues. – Flexible work environment. - Nice office.
Cons
– No infrastructure for AI work: no GPUs, no development environments, no real tooling. – No stakeholder involvement in use case definition — you’ll work in a vacuum, with unclear goals and no business sponsor. – AI projects are poorly defined, often created just to justify the team's presence, not because they solve real problems. – Strong preference for buying vendor solutions instead of building internal capabilities — even when the solutions are misaligned. – No ownership, no budget, no strategic autonomy for the AI team. – You will not be trusted to do your scientific work. You’ll be expected to execute pre-decided, methodologically weak initiatives. – Decisions are made by non-technical leadership, with no background in AI or data science. – Talent is ignored, underutilized, or simply displaced by prepackaged tools. If you’re a real Data Scientist, ML Engineer, or AI technical expert looking to do meaningful work — look elsewhere. This is not a place where AI happens. It’s a place where AI is outsourced and mimicked, not practiced.