Pros
- Everything is systematic
- Fair pay and benefits
Cons
- Toxic environment
- Micromanagement
- They are good in insurance, but in Tech (specially data) the roots are incorrect and not well established and everybody wants to jump to results when the root itself is incorrect
- Incompetent employees are given unofficial authority based on number of years in company
- Politics are in control to the extent that data professionals aren't allowed access to databases and don't even know the dictionary of existing data (Yet you are expected to handle everything and establish data work perfectly while walking blindly)
- Very poor quality of data (Absence of true single source of truth for data), everybody says a number, and the entries recorded on the system are subject to human interventions, exceptions are dismissed, absence of keys, I mean as data scientist you could know assess the quality of the data, but if the details of transactions are input incorrectly how would you know?
- Blame culture (Big time), 0 mentorship, very poor support, when it comes to data work, your load and deadlines are allocated by insurance professionals rather than data professionals (or even worse by incompetent employees who have been around for a while).
- 0 Growth for data professionals, and impractical manual solutions that meet their unreasonable deadlines are preferred over sustainable solutions
- you build models and then with each periodic update you receive data with completely different structures.
- You receive deductions when you are late (and that is fair) however you are expected (and forced) into working after your working hours and during your weekends (for free no overtime)
- 0 respect for personal life or boundaries, how dare you did not pick a sudden call on your weekend
- 0 innovation