Science and Engineering have different intent/purpose but use the same basic "building blocks".

One common way to advance in science is to start from some some conceptual insights (and a review of related work) and some often "curiosity driven" intuitions, propose some hypotheses, which leads to design of experiments (possibly based on tentative models of the System under Study), which leads to the carrying out of experiments which gives data, which, often through statistical techniques and/or more mechanistic models and simulations of those models, which leads to calibrated and validated models. These models can be conceptual, data-driven, or in a plethora of different formalisms, or combinations thereof. The building blocks shared with Engineering are: modelling, simulation (in general, analysis), optimization, optimal experimental design, calibration, validation, ...

The purpose of Engineering is to design, build, maintain, etc. systems from requirements. This is typically a constrained optimization problem. Increasingly, the systems we build contain parts/views/realization technology/environments/... that are not only uncertain, but that need scientific discovery. So, increasingly, traditionally Science workflows, including performing experiments (either designed and controlled, or by collecting data from systems "in the wild"), become part of Engineering practice. The building blocks shared with Science are: modelling, simulation (in general, analysis), optimization (and in particular, Design-Space Exploration, either up-front or "at run-time"), optimal experimental design, calibration, validation, ...