F-1 OPT (H-1B cap-exempt eligible) PhD RNA Biology, MIT (2021) 2-5 Years 2 Weeks Notice Python R Nextflow STAR HISAT2 rMATS LeafCutter SUPPA2 IRFinder DEXSeq edgeR salmon kallisto Seurat AWS RNA Splicing Alternative Splicing Long-Read Transcriptomics Isoform Analysis Disease Mechanisms Neurodegeneration Pipeline Development
#048 F-1 OPT (H-1B cap-exempt eligible)

Bioinformatics Engineer — RNA Biology, Splicing & Transcriptomics

PhD RNA Biology, MIT (2021)

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Experience
2-5 Years
Availability
2 Weeks Notice
Degree
PhD RNA Biology, MIT (2021)
Visa Status
F-1 OPT (H-1B cap-exempt eligible)

Technical Stack

Python R Nextflow STAR HISAT2 rMATS LeafCutter SUPPA2 IRFinder DEXSeq edgeR salmon kallisto Seurat AWS

Domain Expertise

RNA Splicing Alternative Splicing Long-Read Transcriptomics Isoform Analysis Disease Mechanisms Neurodegeneration Pipeline Development

Communication Verified

Passed mandatory 15-min technical explanation interview. Candidate can articulate code logic clearly.

Summary

4 years specialising in RNA splicing and transcriptome dynamics, with a focus on disease-causing splicing dysregulation. Built a bulk + single-cell splicing analysis platform at Arrakis Therapeutics supporting an RNA-targeting small molecule programme. Expert in rMATS, LeafCutter, and long-read isoform sequencing (PacBio MAS-Seq, ONT cDNA). Strong statistical background — comfortable going from raw BAM to biological interpretation without hand-holding.

Experience

Bioinformatics Scientist — Arrakis Therapeutics (2022–Present)

  • Designed multi-tool splicing platform (rMATS + LeafCutter + SUPPA2 ensemble) with integrated concordance scoring; reduced false-positive splicing hits by 35% vs. single-tool approach.
  • Implemented PacBio MAS-Seq long-read isoform pipeline (IsoSeq3 + TAMA) characterising full-length isoform landscape for 12 RNA-targeted disease genes.
  • Built sc-splicing module integrating scRNA-seq (Seurat) with junction-level counts (STARsolo) for cell-type-resolved splicing QTL analysis.

Graduate Researcher — Burge Lab, MIT (2016–2021)

  • Discovered systematic intron retention signatures in ALS patient motor neurons using LeafCutter; findings published in Molecular Cell.
  • Developed spliceviz, a Python/Dash web app for interactive splice graph visualisation used in lab meetings across 15+ groups at MIT.

Selected Publications

  • Nielsen S. et al. “Widespread intron retention in ALS motor neurons driven by TDP-43 loss.” Molecular Cell, 2022.
  • Nielsen S. et al. “Ensemble splicing quantification reduces noise in small-molecule RNA target engagement assays.” RNA Biology, 2024.

Code Quality Notes

Nextflow pipelines use DSL2 subworkflows with nf-test integration tests on GENCODE-subset reference data for fast CI cycles. Python packages typed with pyright in strict mode; Dash app tested with Playwright for end-to-end browser tests. All splice-event statistical outputs include effect size, confidence intervals, and multiple-testing correction method logged explicitly — no silent defaults.

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Reference ID: #048

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