Data Scientist I - Fred Hutchinson Cancer Center
Seattle, WA
About the Job
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. These values are grounded in and expressed through the principles of diversity, equity and inclusion. 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. Fred Hutch is in pursuit of becoming an anti-racist organization. We are committed to ensuring that all candidates hired share our commitment to diversity, anti-racism and inclusion.
A computational scientist position (full-time/fully in-person) at the Bioinformatics Analyst II level is available immediately within The Laboratory for the Study of Metastatic Microenvironments, led by Cyrus Ghajar. The successful candidate will build novel pipelines to uncover clonal and phylogenetic relationships between primary breast tumors and their disseminated seeds. They will apply expertise to integrate different sequencing datasets. They will customize and apply single cell sequencing analyses.
They will also launch a new research endeavor focused entirely on the application of spatial transcriptomics to enumerate, define and transform cellular microenvironments – or niches – within tissues. To do so, they will work closely with members of the Ghajar Laboratory to generate ideal tissue specimens, and profile them using state-of-the-art approaches. They will innovate computational methods to analyze single cell and spatial transcriptomic data, and to visualize these data. And, they will contribute significantly to publications and funding applications showcasing this work.
This is an onsite role working at our Seattle, WA location.
Responsibilities:The Data Scientist (DS) will work under the PI and with laboratory members to:
- Conduct integrative analysis of bulk and single cell tumor datasets, using genetic signatures to establish clonal relationships between the primary tumor and its disseminated seeds.
- Analyze tissue scale spatial transcriptomic datasets spanning liver, brain, bone marrow and other normal and disseminated tumor cell bearing tissues. Trial multiple workflows and develop custom models to characterize niches, niche constituents, and niche occupancy based on protein and transcript expression.
- Adopt and develop data visualization approaches necessary to display single cell transcriptomic and spatial data thoughtfully and intuitively.
- Partner with researchers to collectively define the best experiments and conditions to generate data from, and to test hypotheses shaped by these data.
- Independently generate figures and written sections to document methods and results for manuscripts, presentations, and grant applications.
- Drive publications resulting from the analyses described above.
- Conduct best programming practices such as version control, annotation, data organization, etc, and work towards standardized analysis pipelines for the laboratory. Provide training and support as lab members analyze and interpret results.
The ideal candidate will have a PhD in bioinformatics, computational biology, genetics, data science or related field. They will have extensive experience programming in R and/or Python. They will apply this expertise to build upon existing computational tools – and invent others – to analyze an array of tissue-scale, cellular and subcellular features present in single cell and spatial transcriptomic data sets. They will be adept at displaying these data in an intuitive an artful manner. They will also generate theories and testable hypotheses based on these data, and help guide follow-up studies to answer what we view as some of the most fundamentally important questions in cell biology.
MINIMUM QUALIFICATIONS:
- Master’s degree or PhD in bioinformatics, statistics, computational biology, genetics, data science or related field with at least two years’ direct experience in computational analysis of large single cell sequencing-based molecular data sets.
- Experience with programming in support of a laboratory data management and analysis, clinical trials or statistical programming in a clinical research setting.
- Knowledge of immunology laboratory methods (immunoassays, flow cytometry).
- Proficiency in R and/or Python and version control (Git, GitHub, etc.).
- Demonstrated ability to generate and customize common data visualizations (UMAP/t-SNE, volcano plots, Circos plots, etc).
- Excellent communication skills (both written and verbal) and critical thinking skills are essential.
PREFERRED QUALIFICATIONS:
- PhD in bioinformatics, statistics, computational biology, genetics, data science or related field
- Direct experience can include phylogenetic analysis of evolution on a cellular scale, analysis of single cell RNA-seq data with multiple contrasts, development of custom data visualization approaches, analysis of single cell multi-ome data, integration of data across multiple modalities (e.g., epigenetic profiling and RNA-seq), and so forth.
- Demonstrated experience with integrating large single cell datasets, as evidenced by contributing authorship on peer-reviewed publication(s).
- Experience with larger-scale cancer genomics, epigenomics and transcriptomic data, as evidenced by contributing authorship on peer-reviewed publication(s).
- Experience with MHC binding prediction algorithms.
- Some exposure to high-dimensional spatial data sets.
- Skilled at data visualization and complex statistical analysis.
- Ability to learn new tools and content quickly and independently.
- Ability to work independently and in a team.
- Familiarity with commonly used Bioconductor packages.
A statement describing your commitment and contributions toward greater diversity, equity, inclusion, and antiracism in your career or that will be made through your work at Fred Hutch is requested of all finalists.
This position is eligible for a sign on bonus and relocation.
The annual base salary range for this position is from $93,101 to $139,630 and pay offered will be based on experience and qualifications.
Fred Hutchinson Cancer Center offers employees a comprehensive benefits package designed to enhance health, well-being, and financial security. Benefits include medical/vision, dental, flexible spending accounts, life, disability, retirement, family life support, employee assistance program, onsite health clinic, tuition reimbursement, paid vacation (22 days per year), paid sick leave (up to 30 calendar days per occurrence of a qualifying reason), paid holidays (up to 13 days per year), paid parental leave (up to 4 weeks), and partially paid sabbatical leave (up to 12 months for professor track faculty, up to 6 months for other scientific staff).