Bioinformatician

The Bioinformatician conducts extensive reviews of genetics-related literature and data to understand the significance of genes and their variants. They play a pivotal role in classifying gene-disease associations and developing software tools that enhance the curation of genomic data. Additionally, this professional facilitates collaborative research projects, supports clinical working groups, and contributes to scholarly publications in the field of genomics.

• Data Science and Engineering Expertise:
  Apply data science and engineering expertise to formally specify common data models mapping scientific data with a focus on the types of questions researchers ask.
• Collaboration with Scientists:
  Collaborate with scientists to distill scientific questions and data into the computable entities of a data model.
• Software Engineering
  
  Collaboration:
  
  Work side-by-side with software engineers to maximize the cross-compatibility, searchability, and interoperability of datasets.
• International Standards
  
  Collaboration:
  
  Collaborate with international standards groups to strive for interoperability with data across the globe.
• Data Science and Bioinformatics:
  Apply and grow data science, bioinformatics, and domain experience to solve data management and access challenges.
• Learning New Domains:
  Learn new domains through collaborations with a wider community of computational biologists and biomedical researchers.
• Independent Work Structure:
  Juggle multiple deadlines and structure work independently.
• Statistical Analysis Support:
  Support statistical analysis of in vivo and in vitro studies and lead data science functions of NGS.
• Literature Consultation:
  Consult and search the scientific literature regularly.
• Communication
  
  Skills:
  
  Communicate computational methods and results to non-computational researchers in associated groups as well as with the department at large.

• Collaboration:
  
  Collaborate with molecular biology, protein screening, biochemistry teams, and others on a project-to-project basis.
• Multiomic Analysis Pipeline:
  Create a robust and reproducible multiomic analysis pipeline covering NGS, RNA, protein expression, and proteomics data.
• Data Interpretation:
  Interpret and analyze data with the latest statistical techniques and disseminate the results to other scientific teams.
• Stakeholder Support:
  Work with stakeholders to support data infrastructure needs while assisting with data-related technical issues.
• Data Generation Support:
  Accelerate data generation by supporting protein and genomic screening team’s efforts.
• Quality Improvement:
  Improve the quality and consistency of routine genomic and biochemical analysis.
• Sample Preparation:
  Prepare samples to generate high-quality, accurate, and dependable results.
• R&D Contribution:
  Contribute towards generating key data and insights that will drive R&D strategy.
• Decision-Making:
  Drive decision-making for the tools and techniques our data platform should adopt to achieve goals.
• Project Forecasting:
  Forecast project finalization and communicate service project status updates to sales and customers.
• Computational Analysis:
  Utilize a variety of computational and/or machine learning approaches for analyzing large-scale NGS and other datasets.
• Target Identification:
  Target identification & validation for drug discovery and development programs.
• Tool Development:
  Develop and optimize computational tools, pipelines, models, and approaches.
• Project Management:
  Manage timelines and deliverables on projects and organize and document analysis strategies.
• Data Interpretation:
  Work with molecular biologists to help interpret data and optimize experimental approaches.
• Team Leadership:
  Lead the bioinformatics team within QIAGEN Genomic Services, which provides high-quality, customized sample-to-result services for clients: from nucleic acid isolation to qPCR, NGS, and bioinformatics.
• Data Analysis:
  Analyze biological data with QIAGEN’s own software solutions and other bioinformatics and statistical methods.
• Pipeline Development:
  Develop, validate, document, and deploy end-to-end bioinformatics analysis pipelines.
• Collaboration:
  
  Collaborate with QIAGEN Genomic Services lab scientists and product management.
• Customer Support:
  Assure pre- and post-sales customer support for experimental setup and NGS data analysis results.
• Tool Development:
  Contribute to maintaining and developing DNA synthesis design tools.
• Pipeline Maintenance:
  Aid in maintaining and developing Variant Detection and De Novo NGS Pipelines.
• Python Scripting:
  Develop Python scripts for R&D automation and analysis and serve as an example to Codex DNA’s culture.
• Test-Driven Development:
  Use Test Driven Development to maintain and develop Bioinformatic APIs.
• Literature Review:
  Keep up to date with scientific literature in the areas of strategic interest to Codex DNA.
• Data Analysis:
  Analyze multiple data sets generated internally, as well as publicly available data sets, in an…