Senior Computational Biologist
About the Role
As a senior computational biologist, you will lead the design and optimization of computational models and bioinformatics pipelines, driving our research and technology initiatives. You will analyze large-scale biological datasets, such as single-cell sequencing, Perturb-seq, spatial transcriptomics, and multi-omics data, to uncover insights that inform experimental design and technological innovation. Working closely with wet-lab scientists and engineers, you will translate experimental data into actionable strategies that advance our research. You will also develop new assays and technologies, ensuring they are based on rigorous computational analysis. Additionally, you will mentor junior scientists, building a strong and collaborative team.
About You
You are a highly motivated professional with a strong background in single-cell sequencing.. You have expertise in experimental design and statistical modeling. You work well with wet-lab biologists, driving scientific progress through effective collaboration. You are conscientious, detail-oriented, and organized. You communicate clearly in both writing and presenting. You thrive in fast-paced environments, delivering rapid, actionable insights while maintaining scientific rigor.
What You'll Do and Learn
- Lead the analysis of high-dimensional biological data, including single-cell sequencing and other novel NGS assays.
- Collaborate with experimental biologists to design and optimize experiments, ensuring rigorous data collection and analysis.
- Design and implement robust statistical models and tests to derive meaningful insights from complex datasets.
- Contribute to the development of new assays and technologies, providing computational expertise to enhance experimental outcomes.
- Develop and maintain bioinformatics pipelines, ensuring they are scalable, reproducible, and efficient.
- Implement software engineering best practices, including version control, code review, and continuous integration, to ensure high-quality, maintainable code.
- Utilize Docker and related pipeline technologies to create reproducible and scalable analysis environments.
- Apply deep learning models to biological data, improving prediction accuracy and understanding of biological processes.
- Communicate complex computational results and methodologies clearly to interdisciplinary teams, including biologists, software engineers, and data scientists.
Minimum Qualifications
- Ph.D. in Computational Biology, Bioinformatics, Computer Science, or a related field, or equivalent industry experience.
- Extensive experience in single-cell sequencing analysis, with a strong preference for experience with Perturb-seq.
- Proficiency in R or Python for statistical modeling, data analysis, and software development.
- Strong understanding of experimental design principles and statistical testing methodologies.
- Familiarity with software engineering practices, including version control (e.g., Git), code review, and continuous integration.
- Experience with Docker, Nextflow, and related tools for creating reproducible computational pipelines.
- Excellent verbal and written communication skills, with the ability to convey complex information to a broad audience.
- Experience in the technical development of assays and new biotechnological methods is highly desirable.
Preferred Qualifications
- Familiarity with additional programming languages and tools (e.g., Bash, SQL).
- Experience in cloud computing environments (e.g., AWS, GCP) for large-scale data analysis.
- Experience with deep learning techniques and their application to biological data.
- Prior experience in leading or mentoring junior scientists or bioinformaticians.
Compensation
The specific compensation package for this role depends on experience, qualifications and level. We anticipate the compensation range to be $150,000 to $250,000 per year.
Xaira Therapeutics is an equal opportunity employer. We thrive on diversity and collaboration, and we welcome candidates with diverse backgrounds and experiences.
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