App Orchid reviews

Nothing as of now .

This is one of the worst places to work if you're trying to build a career as a Data Scientist. The work rarely involves real data science — most of the time, it's just repetitive manual tasks unrelated to your core skills. The company presents its product as AI-driven, but behind the scenes, a large portion of the work is being done manually. There’s no clear product or technology vision. If a customer asks for a feature, it's immediately promised without evaluating feasibility or involving the technical team. Instead of thoughtful planning, the company tends to react to requests without understanding the impact. A majority of Data Scientists are underutilized and treated more like execution resources. A small group, typically those who’ve been around for a long time, get some technically relevant work — but even they face unrealistic expectations, often working overnight to deliver. Learning and professional growth are not prioritized. In many cases, people are hired mainly for the brand value of their degrees, rather than for their expertise. Most of the tech stack is Java-heavy. Even if a Data Scientist builds and tests a solution in Python, the engineering team will often reimplement it in Java. If something breaks, the Data Science team has to read Java logs, debug in Python, and then explain the issue to the engineering side — a frustrating and inefficient process. Management is largely disconnected from developments in data science and doesn’t actively seek input from the technical team. Decisions are often made top-down or based on customer demands, without internal discussion or planning. There are long stretches where employees have no meaningful work, and over time, this leads to skill atrophy. Even when you proactively ask for tasks, you might be given low-value assignments like documentation, which are often forgotten or reassigned without acknowledgment. The leadership appears to be in a holding pattern, seemingly waiting for an acquisition. In the meantime, the external narrative is that everything is on track, while internal issues go unresolved. There is noticeable inconsistency in how policies are applied. Some employees, especially those with close ties to senior leadership, bypass standard hiring processes and enjoy more flexibility with office policies. Meanwhile, others are closely monitored. The HR team appears to overlook such discrepancies, which adds to the frustration. Lastly, be cautious about online reviews. It’s not uncommon to see sudden bursts of overly positive reviews shortly after negative ones are posted, likely to balance the company’s rating. These can paint a misleading picture, especially for roles in data science. Summary: If you care about your skills, learning, and long-term career growth, it’s best to look elsewhere.