Job DescriptionThis position will serve as a senior computational scientist responsible for the analysis, integration, and interpretation of large-scale single-cell, multiomic, and genomic datasets generated by the laboratory. The individual will play a central role in transforming raw sequencing data into biologically interpretable models of human brain development and neuropsychiatric disease. The role includes end-to-end ownership of computational workflows spanning raw sequencing processing, genotype-based demultiplexing, single-cell and spatial transcriptomic analysis, and multiomic data integration. The scientist will lead the development and maintenance of reproducible analysis pipelines and will be responsible for ensuring consistency across large, heterogeneous datasets derived from multiple genetic and pharmacologic disease models.
A major component of the position involves integrating diverse datasets-including scRNA-seq, snATAC-seq, multiome, and spatial transcriptomics-with human fetal and postmortem reference atlases to define conserved and disease-specific cell states. The individual will also contribute to the identification of regulatory programs and candidate signaling pathways for experimental validation in organoid systems. This role requires close collaboration with experimental scientists to iteratively refine hypotheses, prioritize perturbation targets, and guide experimental design based on computational findings.
https://profiles.icahn.mssm.edu/deepak-kaji
ResponsibilitiesData Processing & Infrastructure- Process raw sequencing data (FASTQ), including alignment and quantification for single-cell and single-nucleus assays
- Perform genotype-based demultiplexing using tools such as cellsnp-lite and Vireo (or related methods)
- Build and maintain reproducible, well-documented analysis pipelines
- Genotype & VCF Handling (critical component)
- Generate, curate, and harmonize VCFs from whole-genome sequencing and SNP array data
- Perform quality control, filtering, and format standardization across genotype sources
- Implement and troubleshoot imputation workflows and integration of mixed genotype datasets
- Ensure robust genotype-transcriptome linkage for accurate demultiplexing and downstream analyses
Single-Cell & Multiomic Analysis- Analyze scRNA-seq, snATAC-seq, multiome, and spatial transcriptomics datasets using modern frameworks (e.g., Scanpy, SnapATAC2, Squidpy, Pegasus or related tools)
- Perform dataset integration using probabilistic and deep learning approaches (e.g., scVI, scANVI or similar models)
- Conduct differential expression and variance partitioning analyses across complex designs (donor, condition, batch)
- Perform trajectory inference and lineage mapping across developmental systems
Integrative & Translational Analysis- Map organoid-derived cell states to in vivo fetal and postmortem datasets
- Identify conserved transcriptional and regulatory programs across genetic and pharmacologic disease models
- Infer gene regulatory networks and candidate signaling pathways
- Collaborate closely with experimental scientists to design experiments and prioritize perturbation targets
Qualifications- Masters degree or equivalent in computational biology, bioinformatics, genomics, or a related field - Ph.D. in a scientific domain preferred.
- Beginner level, with some experience in a scientific/academic computing environment or equivalent preferred.
- Strong programming skills in Python (required); familiarity with R strongly preferred -
- Demonstrated experience working with single-cell genomics data -
- Strong quantitative and statistical background -
- Ability to work both independently and collaboratively in a highly iterative, interdisciplinary environment -
- Interest in biological interpretation and hypothesis generation, not just data processing
