Are you looking for a patient-focused company that will inspire you and support your career? If so, be empowered to take charge of your future at Takeda. Join us as a Senior Scientist, Computational and Statistical Genetics in our San Diego office.
Here, everyone matters and you will be a vital contributor to our inspiring, bold mission. As a Senior Scientist, Computational and Statistical Genetics working on the Computational Biology team a typical day will include:
The Senior Scientist, Computational and Statistical Genetics will be part of a team focused on computational genomics and computational biology support of discovery research and translational science. The Senior Scientist will apply expertise in statistical genetics, computational biology and genomics to analyze biobank, population scale molecular profiling and phenotype data for the identification and characterization of genes, networks and pathways involved in normal and perturbed physiology in support of reverse and forward translation across multiple therapeutic areas of interest of Takeda. This information will be used to understand the molecular basis of disease pathology and the mechanism of action of drugs, and ultimately be used for selection and progression of drug targets and assets.
- Applies statistical genetics analyses to large scale internal and external genetics and genomics data to support or falsify target hypotheses and support patient and indication selection and biomarker development.
- Performs computational analysis of WES, WGS, RNA-seq, other OMICS and real-world data from internal, collaboration and public sources. Applies unsupervised and supervised machine learning and other advanced algorithms to extract novel biologically meaningful information.
- Leads Takeda Computational Biology engagement in external consortia, collaborates with internal and external stakeholders, accountable for success of the platforms; use her/his scientific and technical expertise to lead implementation of pipelines and algorithms
- Integrates genomics, genetics, epigenetics, proteomics and literature data to strengthen understanding of diseases and treatment perturbations.
- Provides analysis and interpretation of data, specifically for the selection of new drug targets and indications
- Writes study reports and presents data effectively in all settings and with participants of all levels of the organization.
EDUCATION, EXPERIENCE, KNOWLEDGE AND SKILLS:
- PhD or equivalent in statistical genetics, computational biology or similar area with Post Doc and at least 5 years of working experience after post-doc
- Has a solid background in basic
- cellular and molecular biology with an understanding of a range of disease areas including rare diseases, neurobiology, gastroenterology, immunology and oncology. Exposure to a wide variety of therapeutic areas such as neuroscience, gastroenterology and oncology is a plus.
- Fluent in the use of R, Bioconductor, Python, and/or other languages commonly used for statistical genetics and computational genomics analyses.
- Must be expert in developing novel algorithms and pipelines; expertise in large data handling, HPC/cloud environment and bioinformatics tools and databases
- Must be expert in utilizing of various types of human genetic datasets (e.g. GWAS, CNVs, Rare variants, etc.) and has hands on experience in using public human genetics and epigenetics databases.
- Must be expert in statistical genetics with solid understanding in frequentist or Bayesian inference, Mendelian randomization and polygenetic risk score calculation. Familiarity with genetics epidemiology is a plus.
- Must be expert in sequencing data analysis such as RNAseq, DNAseq, scRNA-seq, WES, etc.
- Must have a proven track record in the analysis of large OMICS data
- Is able to develop creative methods for integration of human genetic, epigenetic, gene and protein expression data
- Must be expert and able to apply advanced machine learning methods such as Hidden Markov Chain, Elastic net, neural nets and deep learning algorithms.
- Must be able to apply methods of unsupervised machine learning algorithms such as WGCNA, K-means, Hierarchical, DBSCAN, and/or Spectral clustering, of dimensionality reduction algorithms such as Non-negative Matrix Factorization (NMF), Principal Component Analysis (PCA), Independent Component Analysis (ICA), Manifold Learning and similar techniques.
- Accountability takes responsibility for his or her own performance and accepts full ownership of issues, problems, and opportunities, regardless of the source.
- Comfort with initiating analyses without always having a clear direction planned in advance
- Demonstrated creativity and innovation, including ability for divergent thinking and the propensity to question to traditional methods, processes, and products, as well as build on others' ideas.
- Excellent communication, interpersonal sensitivity, and negotiating skills
- Carrying, handling and reaching for objects up to 25 lbs.
- Able to work in a lab environment
- Up to 30% travel, both domestic and internationally may be required.
Empowering Our People to Shine
Takeda is an EEO employer of minorities, women, disabled, protected veterans, and considers qualified applicants with criminal histories in accordance with applicable laws. For more information, visit
Learn more at takedajobs.com.
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San Diego, CA