Associate Computational Scientist-Psychiatry Dr. Kaji

Mount Sinai Health System•New York, NY

11d

About The Position

This 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.

Requirements

Masters degree or equivalent in computational biology, bioinformatics, genomics, or a related field
Beginner level, with some experience in a scientific/academic computing environment or equivalent preferred
Strong programming skills in Python (required)
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

Nice To Haves

Ph.D. in a scientific domain preferred
familiarity with R strongly preferred

Responsibilities

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
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
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
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