I interviewed for the Data Science Manager — Simulation and Digital Twins role at Tesco. The role itself was highly relevant and interesting, especially because it appeared to require a combination of data science, simulation, digital twins, optimisation, and applied AI experience. My concern is with the recruitment outcome and how the evaluation appeared to be handled. From my perspective, the interview discussions went well and my background was strongly aligned with the role: large-scale simulation, agent-based modelling, digital-twin systems, optimisation, transport/logistics modelling, and applied AI research. However, the final outcome did not seem to reflect that alignment, and the reasoning provided did not give me confidence that the specialist nature of the role had been properly assessed. For a role advertised around simulation and digital twins, I expected the process to evaluate depth in modelling, system design, scientific computing, stakeholder delivery, and real-world decision-support applications. Instead, the process felt somewhat generic and not fully calibrated to the actual technical requirements of the position. Pros: The role sounded genuinely interesting and strategically important. Tesco appears to be investing in advanced analytics, simulation, and operational decision-support capabilities. Cons: The recruitment process felt insufficiently transparent. The final decision did not appear well aligned with the technical depth required for the advertised role, and the feedback was not specific enough to be useful. Advice to management: For specialist roles such as Simulation and Digital Twins, Tesco should ensure that candidates are assessed by people with enough domain understanding to evaluate simulation, modelling, optimisation, and digital-twin expertise fairly. Clearer feedback would also help candidates understand whether the decision was based on technical gaps, team fit, communication, salary level, or internal hiring constraints