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

The purpose of Science is to understand how some System under Study works. 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, and propose some hypotheses. This 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. This data, often through statistical techniques and/or more mechanistic models and simulations of those models, 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, ... Is is often an "inductive" process: going from data to models.

The purpose of Engineering is to design, build, maintain, etc. systems from requirements. This is typically a constrained optimization problem which starts from models of the (yet to be built) System under Study. These models are the results of scientific discovery, often from centuries ago (such as Newton's laws of motion). The models are used for "virtual experimentation", to explore different design alternatives, using analysis techniques such as simultion. 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, ...