Bioinformatician (System Biologist)

We are seeking a talented Bioinformatician who will join the data analysis team in our efforts to develop and operate Anima’s system biology analyses and pipelines. The work includes ongoing interaction with biologists, software engineers and bioanalysis experts.

Key responsibilities:

• Analyze and integrate a broad range of complex scientific information (e.g. multi-omics, lab experiments, literature, and mRNA regulation knowledge) into higher-level understanding and novel concepts.
• Model and interpret changes between two or more biological OMICs data.
• Customization and development of Bioinformatic analysis pipelines – from raw data to insights. Translate researchers' queries into KG semantics and metadata.
• Effectively communicate technical information in a clear, non-technical language to ensure seamless knowledge transfer.
• Develop new hypotheses for future research projects.
• Trace and stay current with evolving industry trends and best practices and data sources.

Education, experience, and other requirements:

• Ph.D. in system biology/bioinformatics focusing on interpreting mRNA-related data.
• Industry experience - A must
• Deep understanding of Ontology semantics and their usage in modern molecular biological research (RDF, OWL, Property graphs, graph-ML).
• Acquaintance with leading biology portals (e.g. Uniprot, String, NCBI, Ensembl etc.) and their programmatic API usage and file formats.
• Advanced knowledge of statistical methods in the context of biological systems.
• Knowledge of bioinformatic packages used today in Python and R (e.g. SciPy/NumPy/pandas, Biopython, Limma, DEseq2, etc.)
• Data wrangling, manipulation, and processing skills to convert data between formats, clean data, process multiple data files, and extract data.
• Attention to details, time management, proactivity, and organization skills are a must.
• Good programming practices, including object-oriented programming, functional design principles, and version control tools (git, Jira, etc.).

Advantageous experience

1. Interpretation of mRNA-related datasets.
2. Modeling knowledge into a unified knowledge graph (KG), including both internal and external data.
3. Machine learning skills.
4. Single cell multi-omics.