- Support translational bioinformatics needs for key oncology clinical projects, including high-throughput data QC, process, analysis and integration.
- Integrate and analyze pre-clinical and clinical, internal and public domain datasets, to generate and evaluate hypotheses for drug combination, new disease indications, and mechanism of action purposes.
- Apply state-of-the-art AI/ML methods for clinical biomarker identification and validation.
- Drive collaborations with internal groups, external partners, including CROs and academia for data analysis and machine learning.
- Support oncology clinical science and translational research teams to achieve translational research and clinical objectives using advanced computational biology techniques.
- Interact closely with biologists and clinicians to conduct analyses, data integration and data interpretation.
- Identify, build, and use bioinformatic pipelines to process and QC omics data types including genomics, transcriptomics, single cell sequencing, proteomics and metabolomics.
- Interpret preclinical and clinical datasets using appropriate computational and statistical methods.
- Create visualizations, interpret high dimensional data and explain results to cross-functional teams.
- Schedule and execute on tasks to meet deadlines.
- Plan and coordinate computational biology activities internally and in partnership with external CROs and academic partners, including complex projects requiring multiple members of the team and organization.
- Prepare and present comprehensive scientific reports to a full range of internal and external audiences.
- Work collaboratively with data scientists and data engineering groups to enhance our computational infrastructure, user interface, query and visualization capabilities.
- Writes study reports and presents data effectively in all settings and with participants of all levels of the organization.
DIMENSIONS AND ASPECTS
- Strong background with high dimensional data analysis derived from Next Generation Sequencing, including some of the following: RNASeq, DNASeq, , epigenetics, and single cell sequencing.
- Knowledge of and experience with machine learning and bioinformatic techniques.
- Hands-on experience developing and implementing bioinformatic pipelines for data analysis on RNASeq, DNASeq, single cell datasets, and metabolomics data.
- Solid knowledge of Unix/Linux, command line interfaces, and fluency in some common scripting and/or programming language (e.g., R, Python, Perl, Java, C / C++).
- Familiarity with parallel computing, relational databases (e.g., SQL) and cloud computing or distributed computing (i.e. AWS)
- Theoretical knowledge of bioinformatics, computational biology; keeps current with emerging trends in bioinformatics and computational biology
- Familiarity with popular public domain data sources and programmatic interfaces
- Experience in designing and conducting computational biology activities to meet program objectives; able to provide input on timelines and resource needs as indicated
- Able to evaluate/develop computational methods based on project needs
- Basic knowledge of biomedical science and technologies outside specific discipline
- Strong scientific understanding of molecular biology and genomics
- Excellent problem solving, communication, presentation, and interpersonal skills
- Independent, self-starting and supportive of team-based research and able to work effectively in a matrix organization
- Ability to work in a team and influence team to make data driven decisions
Decision-making and Autonomy
- Independently manages own workload
- Scientifically independent
- Ability to search literature and come up with innovative solution to difficult problems
- Conducts scientific presentations to internal audiences
- Receives high level instructions on all work, determines methods on new assignments, works closely with manager, may manage junior staff
- Determines methods on new assignments; perform literature search as needed to propose innovation solutions
EDUCATION, BEHAVIOURAL COMPETENCIES AND SKILLS:
- PhD degree in a scientific discipline, with 4+ years experience, or
- MS with 8+ years experience, or