Background To advance early identification efforts, we must detect and characterize neurodevelopmental sequelae of risk among population‐based samples early in development. However, variability across the typical‐to‐atypical continuum and heterogeneity within and across early emerging psychiatric/neurodevelopmental disorders represent fundamental challenges to overcome. Identifying multidimensionally determined profiles of risk, agnostic to DSM categories, via data‐driven computational approaches represents an avenue to improve early identification of risk. Methods Factor mixture modeling (FMM) was used to identify subgroups and characterize phenotypic risk profiles, derived from multiple parent‐report measures of typical and atypical behaviors common to autism spectrum disorder, in a community‐based sample of 17‐ to 25‐month‐old toddlers ( n = 1,570). To examine the utility of risk profile classification, a subsample of toddlers ( n = 107) was assessed on a distal, independent outcome examining internalizing, externalizing, and dysregulation at approximately 30 months. Results FMM results identified five asymmetrically sized subgroups. The putative high‐ and moderate‐risk groups comprised 6% of the sample. Follow‐up analyses corroborated the utility of the risk profile classification; the high‐, moderate‐, and low‐risk groups were differentially stratified (i.e., HR > moderate‐risk > LR) on outcome measures and comparison of high‐ and low‐risk groups revealed large effect sizes for internalizing ( d = 0.83), externalizing ( d = 1.39), and dysregulation ( d = 1.19). Conclusions This data‐driven approach yielded five subgroups of toddlers, the utility of which was corroborated by later outcomes. Data‐driven approaches, leveraging multiple developmentally appropriate dimensional RDoC constructs, hold promise for future efforts aimed toward early identification of at‐risk‐phenotypes for a variety of early emerging neurodevelopmental disorders.