I am writing to express my concerns and confusion following the recent interview process with your company. The job title is Sr. Data Scientist, Data Engineer, Automation, and AI. I found it difficult to understand the actual focus of the role. Are you seeking a true all-rounder at the salary being offered, or is this an overly broad (and potentially ambiguous) title used to attract senior-level candidates and ideas without clear intent? I successfully completed the first two rounds and took on a highly detailed technical assessment, which included: Designing an ERD Writing SQL scripts Building a DBT project Creating a data flow diagram Deriving five clearly defined metrics To deliver this, I went beyond standard expectations by: Creating and hosting the database infrastructure on Amazon Web Services Integrating with local clients and producing ERD diagram and SQL scripts establishing entitiy relationships. Implementing a complete ELT pipeline using DBT (which is a non-trivial and time-consuming task requiring a solid understanding of data modeling and orchestration) Integrating HubSpot and Podio through Make.com as part of the overall solution Despite this effort, the feedback I received stated that I “did not go deep enough into the topic.” This leaves me with several questions: What exactly is the expected level of “depth” for this role? The scope of the assessment required building a working data infrastructure from scratch, which I did comprehensively. If that doesn't meet your expectations, what would? What is the size, quality, and complexity of the dataset you're using? Depth of analysis depends significantly on the richness of the data provided. You cannot expect advanced analytical depth from a constrained or limited dataset. Moreover, I do feel it is important to understand the approach to gauge the analytical abilities of a candidate. To put it in simple words. When you say Do this this and this, it will be done. but if you say I have this, suggest ways to get these or analyse the data set to come up with some metrics gets the analytical abilities. We cannot ask a person to take tram xx and get down at HBF and then say why did you not choose bus or come up with alternate routes. Customer interviews and they we conduct them matter buddies. How do these technical expectations align with the salary being offered? There seems to be a clear mismatch between the depth and complexity of work expected and the compensation level discussed. This makes it difficult to assess whether the role is structured fairly, especially for senior professionals with real-world experience. This shift in focus mid-process created additional ambiguity about the expectations and evaluation criteria. During the interview, I was asked to propose AI use cases—which, in my opinion, felt more like a consulting discussion than a candidate evaluation. The conversation went as deep as feature engineering, which is highly specific and context-dependent. This leads me to ask: What exactly are the expectations for this role? Investing time, effort, and infrastructure to deliver a complete solution—only to be told it lacked “depth”—without actionable or specific feedback is disappointing. Constructive input is critical for professionals to learn and grow, especially when significant effort has been invested. I hope this message is taken in the spirit of transparency, fairness and is deep enough.