Lead BioInformatician
Location:
Jerusalem /hybrid
Our amazing client is an early-stage deep tech startup at the forefront of digital health, developing a groundbreaking Large Language Model for decoding 100% of the human genome, with the potential to analyze whole genome sequencing data in real-time.
We are seeking an experienced Lead Bioinformatician to oversee our computational biology team and drive the integration of clinical and genomic data analysis pipelines. This role requires exceptional leadership in computational biology, and biostatistics, and the ability to bridge complex genomic and clinical data landscapes
Key Responsibilities:
Lead and mentor clinical data scientists and bioinformaticians
Design and implement robust QC frameworks for both clinical semantic data and next-generation sequencing data
Develop standardized workflows for data integration across multiple modalities (genomic, transcriptomic, clinical)
Establish best practices for reproducible analysis and documentation
Collaborate with clinical teams to translate biological questions into computational solutions
Drive innovation in data integration methods and analytical approaches
Required Qualifications:
MSc or Ph.D. (Preferred) in Bioinformatics, Computational Biology, Biostatistics, or related field
5+ years of hands-on experience in genomics data analysis and pipeline development
Strong background in biostatistics with expertise in:
* Statistical modeling and experimental design
* Multiple testing correction
* Batch effect correction
* Power analysis
Demonstrated expertise in:
* NGS analysis (WGS, RNA-seq, single-cell sequencing)
* Clinical data harmonization and standardization
* Version control and reproducible research practices
Programming proficiency in: * R/Bioconductor * Python * Shell scripting * High-performance computing environments
Fluent English mandatory
Track record of developing novel computational methods
Knowledge of semantic web technologies and ontologies
Experience with: * Machine learning applications in healthcare * Cloud-based genomics platforms (Terra.bio, AWS, Azure) * Container technologies (Docker, Singularity) * Workflow managers (Nextflow, Snakemake, WDL)