Basic Function and Scope of Position:
Medivation provides a unique environment for drug development and bioinformatics, including the availability of rich datasets and technologies, the opportunity to work with leading experts in various areas of drug development, and a culture of close collaboration between experimental and computational scientists. As Medivation’s Sr Bioinformatics Scientist, you will work with patient data and develop statistical algorithms, methods, and programs for analysis of clinical genomic sequencing and gene expression data and diagnostic products. You will manage a team that is designing, building, delivering and supporting high throughput bioinformatics pipelines to support our ongoing NGS biomarker discovery studies. The focus of the position is to apply statistical and mathematical modeling for genomic research and diagnostic product development for advancing drugs in the clinic. These genomic data are guiding treatment decisions and the enrollment of patients onto clinical trials of novel targeted therapies. The Sr. Bioinformatics scientist will work closely with R&D scientists, software, database, and outside teams to deliver cutting edge bioinformatics solutions. This position comes with the opportunity and expectation to publish high-impact papers in oncology and bioinformatics.
Tasks and Responsibilities:
Analyze massively parallel signature sequencing (MPSS) clinical genomic data, gene expression and/or next-generation sequencing including RNASeq, Exome, CHiPSeq data for the discovery of novel gene expression patterns, transcripts, novel isoforms, novel gene fusions, mutations etc.
Work collaboratively with research scientists to develop and implement statistical solutions pertaining to gene expression, chromosomal variant analysis, genome annotation and public genomics/genetics databases
Develop Translational Medicine plans (e.g., patient selection and indication biomarkers) and predictive modeling in collaboration with Clinical project teams. Build computational, statistical, machine learning or modeling capability needed to synthesize data into the actionable next steps for the Projects
Design the computing strategy sequencing workflow data management and the computing infrastructure for processing NGS data (local vs. Amazon cloud, etc.) and lead the execution of the computing strategy in a High Performance Computing (HPC) environment for algorithm development and optimization
Differential gene expression analyses, functional genomic annotations & pathway/gene networks; Statistical data analysis to support assay development experiments and guide experimental approaches
Works on complex biological problems in which analysis of situation or data requires a review of identifiable factors. Develop pipelines for large-scale ‘omics data analysis. Assess, test, and implement best practices for ‘omics data analysis. Conduct statistical analysis of a variety of data types including longitudinal, hierarchical, and survival data. Develops tools to integrate commonly used open source bioinformatics software applications.
Develop statistical/mathematical models to identify genomic variants such as chromosomal insertions, deletions, CNVs, or SNPs.
Accurately identify mutations from clinical next-generation sequencing data, characterize the spectrum of genetic mutations in patient tumors, develops and refine filtering criteria for appropriate elimination of false positive mutation calls.
Collaborate with scientific research community to develop advanced analysis method for genomic analysis
Preferred Educational Background:
Ph.D. or Masters in Computer Science, Computational Biology Statistics, Bioinformatics, Physics or related field is highly desirable. For this position, we are seeking an individual with a strong background in a quantitative field, such as statistics, bioinformatics or computer science and a reasonable working knowledge of cancer genomics or molecular biology. Candidates with preparation in biology who can demonstrate competence in quantitative sciences or programming will also be considered.
Preferred Experiential Background:
An ideal candidate will be collaborative, self-directed and possess first-hand experience in NGS analysis and methods as well as the computing infrastructure and data management methods to support NGS. First-hand multi-parametric patient data mining experience on expression, copy number and profiling public & internal datasets for target identification and biomarker discovery. For example, multivariate analysis; dimensionality reduction methods; parametric and non-parametric statistical methods; Bayesian statistics; pattern recognition or classification methods. First-hand experience at integrating publicly or commercially available genetic, genomic, and interaction datasets with novel experimental data to identify testable hypotheses. A background in Oncology, Translational Research, Pathway Analysis, Quantitative Methods for Systems Biology, Network Analysis, or Simulation are a plus.
- A solid knowledge of cancer biology, hands-on experience in large-scale cancer NGS and microarray data QC, management and analysis, e.g., TCGA; familiarity with public genome databases (such as genome annotation, genetic variants, and metabolic and signaling pathways); must be aware of and keep up with newly available public resources and publications
- Fluency in programming (Perl, Python, JAVA, C, C++, BioPerl, Ruby on Rails, and shell in Linux environment); Proficient in statistical data analysis of genomic data using R/BioConductor, SAS, MatLab, etc., with ability to perform statistical analysis, interpret, and effectively communicate results; Experience in development of computational algorithms and bioinformatics software in molecular profiling data analysis; Experience with databases e.g., mySQL and ORACLE, and proficient in SQL as well as nonSQL databases
- Experience with public analysis tools such as BWA, GATK, SAMtools, TopHat, GSEA etc.; Experience with commercial tools such as SpotFire, Partek, GeneSpring, IPA, GeneGo, IPA VA, MuTect etc.; Proficiency with various variant annotation and filtering tools for predicting deleterious or LOF mutations and ranking putative causal variants
- Experience with predictive modeling and system biology modeling (e.g., use statistical and machine learning methods to identify prognostic and predictive biomarker signatures from high dimensional biological data; Bayesian network, machine learning, neural network, decision tree, etc.) and network building (e.g. gene regulation networks, causal networks, protein-protein interaction networks and integration of the above); develop tools and methodologies for unified and practical network/model building involving genetics, metabolomics, transcriptomics, genomics and epigenomics; Generate and refine network/model to describe, predict and validate biological modulations across different spatiotemporal scales; Keen interest and determination to be at the cutting edge of algorithms and model building
- Experience with 1) cloud-based analytical tools, 2) parallel processing, 3) integration of cloud computing with on-premise solutions, 4) data warehouse and databases such as Hadoop, Hbase & Hive, are plussesMust have minimum of 5+ years of experience in a similar position which can include a variety of experiences in Graduate research/Postdoc research in Academic Institutions or Life Sciences, Genomics or Pharmaceutical industries
- Experience with building / supporting / using bioinformatics pipelines e.g. Pipeline Pilot etc.
- Experience with statistical and mathematical modeling with genomic data
- Experience with genomic analysis of sequencing data
- Experience with machine learning, Bayesian statistics, clustering/classification, signal/noise analysis, multivariate analysis, and/or ROC analysis
- Ability and willingness to work in a fast paced environment with excellent time and project management skills
- -Must be able to work well both independently (minimal supervision) and collaboratively with groups from different disciplines with a strong team spirit, excellent interpersonal skills, and a commitment to share data across the organization
- Must have proficient written, communication and presentation skills with the ability to present to both scientific and corporate audiences
- Must be independent and strategically minded
- Strong record of publishing in peer-reviewed journals
Please contact: Hirdesh.email@example.com