This includes developing and applying robust methods for modeling heterogeneous biological contexts (e.g., cell-type-specific perturbation responses, animal, batch or treatment context variability), identifying and correcting for confounders (e.g., cell cycle, ambient RNA, doublets, batch effects etc.), and selecting or designing appropriate positive and negative controls to validate effect sizes and method performance. You''re very proficient in both Python and R for single-cell analysis (e.g., Scanpy, Seurat) and general data analysis, moving fluidly between ecosystems, and you have working familiarity with NGS workflows and common file formats (FASTQ, BAM) sufficient to reason about data provenance and quality - though your focus is on what the data reveals biologically, not on building or maintaining the infrastructure that produces it.