Roche Bioinformatics Contract Engineer/Scientist

Company Description:

Roche Diagnostics is a global leader in healthcare diagnostics, committed to improving patient outcomes through innovative diagnostic solutions. With a focus on precision medicine and personalized healthcare, Roche Diagnostics develops and commercializes advanced diagnostic tests and technologies across various disease areas, including oncology, infectious diseases, and autoimmune disorders. Our mission is to empower healthcare professionals with accurate, timely, and actionable diagnostic insights to support informed clinical decision-making and improve patient care.

Job Description:

We are seeking a talented and experienced AI expert in the field of bioinformatics and computational biology to join our dynamic team. In this role, the successful candidate will play a key role in the development and implementation of AI-driven approaches for the analysis, interpretation, and utilization of molecular data in diagnostic assays and platforms. Working closely with interdisciplinary teams of biologists, computer scientists, and domain experts, the AI expert will contribute to the advancement of Roche’s molecular diagnostic portfolio and drive innovation in precision diagnostics leveraging state-of-the-art methodologies and tools. A strong background in software development and data engineering will be a big advantage for this role.

This could be a hydrid or remote position based on the location in Canada.

NGS Data Analysis: Apply advanced computational methods and algorithms to analyze and interpret NGS datasets, including whole-genome sequencing, exome sequencing, transcriptome sequencing, ChIP-seq, and other relevant NGS applications. Perform quality control, data preprocessing, alignment, variant calling, and annotation.

Tool Development: Contribute to the development of software tools, pipelines, and computational resources for bioinformatics analysis, data visualization, and knowledge dissemination.

Algorithm Development: Design, implement, and optimize AI and machine learning algorithms for analyzing biological data, including but not limited to genomics, epigenomics, transcriptomics, proteomics, and metabolomics data.

Data Integration: Develop methods for integrating heterogeneous biological data sources to extract meaningful biological insights, combine the strengths of different data modalities and facilitate data-driven decision-making.

Predictive Modeling: Build predictive models and computational frameworks to uncover complex patterns in biological systems, and apply them to risk prediction algorithms, disease detection and classification models, treatment response predictors and disease mechanism discovery.

Biomarker Discovery and Development: Apply advanced computational methods to identify, validate, and characterize biomarkers relevant to diagnostic applications, leveraging multiomic data integration, feature selection techniques, and predictive modeling approaches.

Collaboration: Collaborate with interdisciplinary teams of scientists and researchers to understand biological questions, formulate hypotheses, and devise computational strategies to test hypotheses and validate findings.

Innovation and Continuous Improvement: Keep up-to-date with the latest advancements in AI, machine learning, and bioinformatics research, and incorporate relevant methodologies and techniques into project workflows.

Documentation and Communication: Document algorithms, methodologies, and analysis pipelines, and effectively communicate results and findings through presentations, reports, and scientific publications.

Education: A Ph.D. or equivalent degree in bioinformatics, computational biology, computer engineering, computer science, statistics, machine learning, or a related field.

Expertise: Demonstrated expertise in AI, machine learning, and statistical methods, with a focus on their application to biological data analysis. Experience in diagnostic applications and omic data sets is an advantage, but not required.

Programming Skills: Proficiency in programming languages commonly used in bioinformatics and computational biology, such as Python or R, and experience with relevant libraries and frameworks (e.g., TensorFlow, PyTorch, scikit-learn).

Bioinformatics Knowledge: Strong working knowledge of molecular biology, genetics, and bioinformatics concepts, as well as experience with biological databases, tools, and resources.

Problem-solving Skills: Ability to think critically, creatively, and analytically to tackle complex biological problems and develop innovative computational solutions.

Teamwork and Communication: Excellent interpersonal skills and the ability to work collaboratively in a multidisciplinary team environment, as well as effective communication skills to convey technical concepts to diverse audiences.

Publication Record: A track record of scientific publications in peer-reviewed journals or a successful track record of working in the industry demonstrating contributions to the field of AI, bioinformatics, computational biology, or related disciplines. 

Preferred Qualification:

Postdoctoral Experience: Prior postdoctoral or industry experience in computational biology, bioinformatics, or a related field.