Position Summary

We are seeking a Scientist to drive multimodal genomics experiments supporting external BioPharma

collaborations and internal discovery efforts. The Scientist will design, execute, and interpret studies that

combine bulk and single-cell epigenomic and transcriptomic, spatial transcriptomic, and proteomic

(including spatial proteomic) readouts on preclinical models and human patient samples, translating

complex datasets into biological insight for pharma partners and internal programs.

This is a hands-on, independent-contributor role for a scientist early in their industry career who

combines strong cancer-biology judgment with technical depth across multiple genomic platforms and is

comfortable working with computational scientists and project teams.

Key Responsibilities

• Drive end-to-end execution of multimodal genomics studies (e.g., DNA

methylation/hydroxymethylation, chromatin accessibility and occupancy, RNA-seq,

proteomics/phosphoproteomics) from experimental design through data delivery.

• Plan and execute single-cell RNA-seq, spatial transcriptomics, and spatial proteomics

experiments, including sample preparation, platform workflows, QC, and coordination with

computational scientists for downstream analysis of cell types, states, and tissue architecture.

• Translate collaborator and program questions (e.g., mechanism of action, therapeutic response,

biomarker discovery) into fit-for-purpose experimental designs, sample plans, and assay panels.

• Support external BioPharma collaborations: contribute to study scoping, execute studies to agreed

timelines, communicate progress and results to partners, and contribute to reports and

presentations.

• Lead internal discovery experiments that generate and test hypotheses about cancer cell state,

treatment response, and candidate biomarkers, including potential application to liquid-biopsy

(cfDNA/ctDNA) profiling.

• Generate high-quality NGS and, where applicable, mass-spectrometry datasets; perform library

preparation, QC, and sequencing run management, and flag technical or batch effects.

• Integrate multi-modal datasets using R and/or Python, in partnership with computational scientists,

to identify signatures and prioritize candidates.

• Implement and standardize laboratory workflows, including liquid-handling automation, to improve

throughput and reproducibility across projects.

• Maintain rigorous documentation, sample tracking, and data provenance; contribute to SOPs,

technical reports, manuscripts, and patents.

• Mentor and train junior staff and collaborators in experimental design and technical methods as

needed.

Minimum Qualifications -- Please include the full list of your first-author and co-authored publications.

• Ph.D. in Cancer Biology, Molecular Biology, Genetics/Genomics, Biochemistry, or a related field.

• Plus ONE of the following: (a) at least 1 year of relevant industry experience (biotech, pharma,

diagnostics, or equivalent), or (b) a completed postdoctoral fellowship.

• Demonstrated experience with at least two genomic modalities (e.g., epigenomic sequencing,

RNA-seq, proteomics) and with NGS library preparation and QC as evidenced in high quality

publications.

• Working proficiency in R and/or Python for genomic data analysis and visualization.

• Demonstrated ability to work across wet-lab and computational teams, and to communicate results

clearly in writing and verbally.

• Strong desire to drive the utilization of novel technological approaches to answer difficult biological

questions.

Preferred Qualifications

• Hands-on experience with single-cell RNA-seq, spatial transcriptomics and/or spatial proteomics

platforms (e.g., sequencing-based or imaging-based spatial assays, multiplexed tissue imaging),

including FFPE or fresh-frozen tissue handling.

• Familiarity with other high-dimensional platforms such as quantitative mass spectrometry.

• Experience with human patient tissue and preclinical models (cell lines, organoids, GEMMs).

• Familiarity with functional assays (flow cytometry, viability, live-cell imaging, protein-interaction and