Language proficiency constitutes a crucial barrier for prospective international teaching assistants (ITAs). Many US universities administer screening tests to ensure that ITAs possess the required academic oral English proficiency for their TA duties. Such ITA screening tests often elicit a sample of spoken English, which is evaluated in terms of multiple aspects by trained raters. In this light, ITA screening tests provide an advantageous context in which to gather rich information about test taker performances. This study introduces a systematic way of extracting meaningful information for major stakeholders from an ITA screening test administered at a US university. In particular, this study illustrates how academic oral English proficiency profiles can be identified based on test takers' subscale score patterns, and discusses how the resulting profiles can be used as feedback for ITA training and screening policy makers, the ITA training program of the university, ESL instructors, and test takers. The proficiency profiles were identified using finite mixture modeling based on the subscale scores of 960 test takers. The modeling results suggested seven profile groups. These groups were interpreted and labeled based on the characteristic subscale score patterns of their members. The implications of the results are discussed, with the main focus on how such information can help ITA policy makers, the ITA training program, ESL instructors, and test takers make important decisions.