Scientist I Computational Biology
- Employer
- Planet Pharma
- Location
- South San Francisco, California, US
- Salary
- Competitive
- Closing date
- Jan 26, 2022
View more
- Sector
- Academic / Research
- Field
- Informatics / GIS
- Discipline
- Statistics, Biology
- Salary Type
- Salary
- Employment Type
- Full time
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Computational science is a fundamental component approach to developing novel treatments for oncology and inflammatory diseases. We are seeking a talented and motivated scientist to join our vibrant Computational Biology (CB) team. The job demands a knowledge of oncology and/or immunology, statistical analysis, machine learning as well as cutting-edge tools and techniques in bioinformatics.
The ideal candidate will thrive when integrated into project teams and be expected to provide solutions and insights to preclinical and translational programs from a CB perspective. As a CB scientist, you will be helping with experimental design, data analysis and statistical interpretation that will lead to the generation and/or validation of new hypotheses. A deep knowledge of NGS technologies and methods, Statistical analysis, machine learning, and excellent programming skills in Python and/or R is essential for this role. The candidate must be comfortable working independently and collaborating and communicating with scientists at all levels and across disciplines.
Responsibilities include (but not limited to):
The ideal candidate will have the following qualifications:
The ideal candidate will thrive when integrated into project teams and be expected to provide solutions and insights to preclinical and translational programs from a CB perspective. As a CB scientist, you will be helping with experimental design, data analysis and statistical interpretation that will lead to the generation and/or validation of new hypotheses. A deep knowledge of NGS technologies and methods, Statistical analysis, machine learning, and excellent programming skills in Python and/or R is essential for this role. The candidate must be comfortable working independently and collaborating and communicating with scientists at all levels and across disciplines.
Responsibilities include (but not limited to):
- Provide computational biology support for inflammation and/or immuno-oncology programs including discovery biology, translational research, and clinical biomarker studies
- Develop and implement bioinformatic algorithms for integrative analysis and visualization of multi-omics data from various sources and data modalities such as bulk or single-cell RNA-Seq, WES, proteomics, and other biomarker signals from serum, blood, and tissue biopsies
- Develop efficient algorithms for analyzing immune landscapes based on cell-type assignment and/or spatial localization to support our inflammation or immuno- oncology biomarker programs and understanding of the MOA for our drugs
- Work closely with biologists and immunologists, guiding experimental design, assay development and/or optimization, hypothesis generation and validation to support translational and discovery biology efforts
- Participate actively in documenting, maintaining, and improving computational biology tools, pipelines, and applications via GitHub code repository and version control
- Develop web applications such as R-Shiny and/or Python-Streamlit to make complex analysis pipelines and database queries accessible to non- bioinformatics scientists
- Actively participate in presenting novel research and computational algorithms in conferences and peer reviewed journals.
- Stay up to date with the latest research publications and present in company's R&D journal club
The ideal candidate will have the following qualifications:
- Ph.D. in bioinformatics, computational biology, genomics, immunology, cancer biology, statistics, computer science, or a related discipline.
- At least two years of postdoc or industry experience are preferred, but not essential.
- Must have experience working with large genomic data sets, planning Next Generation Sequencing experiments and analyses.
- Experience in analyzing and integrating multi-omics bulk and/or single-cell datasets is strongly preferred.
- Proficiency in programming languages such as R and/or Python in a Unix environment is required.
- Working knowledge of machine learning algorithms, exploratory data analysis, and statistical modeling and techniques is required.
- Experience in data analysis using cloud computing infrastructure such as AWS
- Excellent communication, interpersonal, written skills, and cross functional teamwork are essential.
- Must be comfortable handling multiple concurrent fast paced projects.
- Experience in predictive/response biomarker identification and translational research with applications in patient datasets and/or clinical trials would be a significant plus.
- Knowledge of Data Science applications in drug discovery and development, especially in immuno-oncology field is highly valued.
- Favorable consideration will be given to candidates with a strong working knowledge of immunology and/or oncology.
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