Summer jobs have become an increasingly popular topic among high school students these days. They provide invaluable enrichment opportunities to make money and gain experience and skills. However, due to the outbreak of coronavirus, it is more challenging for high school students to identify the most appropriate summer jobs for them. We first identify related variables and collect data useful to our model. We consider 22 different summer jobs and consider variables including income, company size, risks, comfort, and skills to be gained. For the overall model, we adopt an Analytic Hierarchy Process to evaluate the weights or relative importance of each variable for each individual. Combining the weights we calculate from both models, we then obtain the overall weighting used for each individual. However, considering students usually want to have an array of open options to choose from, we utilize K-Means clustering rather than simply returning the one single "best"job. Then, the best cluster of job options is output to our user. In regards to the fictional characters, we develop 10 different characters that we believe are highly representative of the US high school student population. Application of our developed model onto these fictional characters indicates that our model is fairly effective in identifying the most appropriate summer jobs for students. © 2021 ACM.