Staff Scientist

Fred Hutchinson Cancer Center•Seattle, WA

8d•$84,406 - $191,755•Onsite

About The Position

Fred Hutchinson Cancer Center is an independent, nonprofit organization providing adult cancer treatment and groundbreaking research focused on cancer and infectious diseases. Based in Seattle, Fred Hutch is the only National Cancer Institute-designated cancer center in Washington. With a track record of global leadership in bone marrow transplantation, HIV/AIDS prevention, immunotherapy and COVID-19 vaccines, Fred Hutch has earned a reputation as one of the world’s leading cancer, infectious disease and biomedical research centers. Fred Hutch operates eight clinical care sites that provide medical oncology, infusion, radiation, proton therapy and related services, and network affiliations with hospitals in five states. Together, our fully integrated research and clinical care teams seek to discover new cures to the world’s deadliest diseases and make life beyond cancer a reality. At Fred Hutch we value collaboration, compassion, determination, excellence, innovation, integrity and respect. Our mission is directly tied to the humanity, dignity and inherent value of each employee, patient, community member and supporter. Our commitment to learning across our differences and similarities make us stronger. We seek employees who bring different and innovative ways of seeing the world and solving problems. A Staff Scientist position in the Nelson, Haffner, Li and Ha research groups located in the Human Biology Division and Computational Biology Program is available immediately. We have established a research team working with interdisciplinary collaborators who have expertise in genomics, genetics, pathology, experimental therapeutics, cancer biology, and clinical cancer research. We are seeking a highly motivated individual with experience in applying bench laboratory experimentation and computational and analytical approaches to the study genomics and epigenetics of cancer. The specific focus for this position involves developing assays using ctDNA/cfDNA in cancer diagnostics. Candidates who are excited about driving projects and participating in teams that develop and apply technologies for interrogating cancer genotypes and phenotypes through bioinformatics, data science, genomics, spatial biology and drug development are encouraged to apply. The position has a competitive salary with excellent benefits.

Requirements

A PhD and/or MD degree in a biomedical science discipline such as biology, molecular biology, biochemistry, biophysics, computer science, computational biology, biostatistics, biomedical engineering, or other related fields
Experience and expertise with programming languages and environments such as R, Python, others.
Demonstrated experience in liquid biopsy assay development (cfDNA/ctDNA).
Hands-on experience with NGS-based assays, including epigenetic assays such as cfDNA methylation sequencing.
Strong background in processing and analyzing large-scale genomic datasets (WGS, methylation, cfDNA fragmentation, variant calling).
Significant experience with Python, R, and workflow managers (bash, Snakemake, Nextflow).
Expertise developing machine learning and deep learning models.
Experience with assay optimization and analytical validation (LOD, specificity, reproducibility).
Understanding of ctDNA biology, cfDNA fragmentation, tumor heterogeneity, and multi-omic biomarker design.
Ability to interpret complex biological results and communicate them clearly.
Publications of peer reviewed articles in the field of genomics, genetics, cell engineering, or developmental biology
A minimum of 3 years of direct experience in molecular biology, developmental biology, genetics, epigenetics, computational genomics, or related fields

Nice To Haves

Demonstrated success developing cost-effective, scalable liquid biopsy assays.
Hands-on experience with ddPCR, low-pass WGS, targeted panel design, library prep, or other quantitative molecular platforms.
Experience translating computational methods to the clinic, including CLIA/CAP LDT development.
Experience working with multi-omic biological signals for cancer detection, subtyping, or therapeutic profiling.
Familiarity with cloud-based infrastructure for large-scale genomic analysis.
Knowledge of validation frameworks and guidelines.
Strong track record of peer-reviewed publications, high-impact manuscripts, patents, or translational tools relevant to position.
Experience collaborating with clinicians, industry partners, or translational research teams.

Responsibilities

Conduct research independently as well as contribute to research being done by other team members.
Assist in project development, design, and management
Participate in the training of undergraduate, graduate, and postdoctoral scientists.
Lead mining and integration of large-scale multi-omic datasets (WGS, methylation, chromatin, cfDNA) to identify quantitative biomarkers for ctDNA detection and tumor characterization.
Develop, refine, and benchmark computational pipelines for biomarker selection using statistical modeling, machine learning, and deep learning approaches.
Build robust cloud-based workflows and scalable pipelines for large-scale genomic data processing.
Innovate and evaluate new metrics for ctDNA quantification (e.g., methylation-based signals, fragmentation, gene expression inference).
Design and optimize ctDNA assays using low-cost molecular platforms such as low-pass WGS, EM-seq, targeted sequencing, ddPCR and others.
Translate computational biomarkers into practical assays via targeted primer/panel design, reaction optimization, and QC framework development.
Conduct analytical validation studies including LOD determination, precision, reproducibility, and specificity.
Contribute to development of prognostic biomarkers for clinical applications.
Collaborate with CLIA/CAP laboratory teams to integrate assay workflows into clinical pipelines, including documentation for LDT deployment.
Support translational studies in collaboration with clinicians and academic/industry partners (e.g., tumor detection, subtyping, therapeutic stratification).
Lead validation efforts on real-world biospecimens such as plasma.
Candidates with strong interest and/or expertise in any of these research areas are highly encouraged to apply Developmental biology; cancer genomics, liquid biopsies, tumor evolution/heterogeneity, epigenetics, single-cell omics Application of statistical modeling, algorithm design, artificial intelligence and machine learning, explainable AI to study cancer and genetics.
Analysis of large, complex genome, epigenome or transcriptome data.