Interviewed within two stages. One classic first touch point with an HR recruiter discussing overall experience, and following up with what was described as a technical interview stage. Interviewed by a Digital - no data science professional and a junior data scientist. After receiving hr feedback, I wanted to share some thoughts on the process. The job description and Semrush’s listed interview process indicate that this was intended to be a technical interview, meant to assess candidates on deep technical expertise in statistical analysis, causal inference, MMM modeling, and experimentation. So, as it was framed on Semrush website, the objective was to dig dive in details on technical expertise, portfolio of statistical and MMM knowledge, and so reward the best “showcase” of technical expertises. As matter of fact, by the HR presentation of the process and Semrush website, the more high-level, business oriented conversation was to be expected to a later stage with the head of analytics. Usually, from my tenure with technical departments of corporations (GE, Google, Amazon..) , stages framed like these reward technical acumen with detailed responses, while penalising short, detail poor replies. Also, in no whatsoever moment the questions were framed like discussion with C-levels or STAR approaches (except maybe one where was specifically asked to explain p-value to a C-level, which is when I keep it short and sharp). However, during my interview: One of the interviewer, mostly read from a script, rather than engaging in an in-depth technical discussion of related questions of Causal designs, Marketing Mix Modeling, AB experiments, ecc... Being a technical stage, a candidate would expect to discuss and validate technical frameworks, validity of the statistical and machine learning assumptions, and anything else related to building robust methodologies. The feedback I received from the recruiter focused only on being “sharper with answers for C & D-level stakeholders”, which, while relevant for business discussions, does not align with what was expected from what was framed as a technical interview nor was painted as the objective of this stage on Semrush website. The process did not assess expertises in the stated key areas of the job description, which was mostly painted as hands-on, highly focused on machine learning and statistical design, which raises concerns about alignment between the interview stages and the selection criteria. Most of the questions were basic KPI related (Which KPIs For Campaign/Retention/Up-Cross sell). There were no dig-dive in areas such as advanced models, causal or statistical inference, attribution, Machine Learning pipelines, Experimental design settings. In addition to not having a question on any of the topics mentioned from the script the interviewer was following, there were also no spontaneous questions, leaving most of the interview out of the scope of advanced modeling/experimental assessment. For a described senior-level role requiring a significant technical foundation, I believe a more structured and role-specific approach to technical interview stages would improve the candidate experience, alignment between parts expectations and preparation, and ensure that hiring decisions are based on actual technical competencies, at least at these stages, rather than C-levels communication style, which again was not described as the objective for the stage interviewed. Overall, there is a derived perception from the interview questions and process that the causal inference/data science/experimental maturity and knowledge base in the marketing analytics department could not match the one promoted for the job position. Also, interestingly, the same position was reposted shortly after my interview, despite HR feedback suggesting that there were other candidates passing on to the other stages in the pipeline. This raises some concerns about transparency in the hiring process and the reliability of the feedback received.