Staff Scientist - Assistant Level (Bioinformatics)

About the Role
The Staff Scientist - Assistant Level (Bioinformatics) supports research within specific areas of research programs and performs a variety of procedures using established methods and techniques. The incumbent conducts moderately complex experiments, critically evaluates scientific literature and findings, performs experiments, and interprets data.

Responsibilities

• Under the general supervision of more senior scientific staff, performs research tasks of a moderate scope.
• Performs scientific research at the highest standards.
• Conducts experiments, analyzes, and processes data.
• Collects and summarizes scientific data/information for experiments and archiving.
• Keeps accurate records of experiments, checks data, and informs on discrepancies.
• Assists with evaluating and testing new processes.
• Complies with safety regulations/standard protocols.
• Collaborates with other members to apply the results of research and/or develop new techniques or practices.
• Prepares research results for journals and report publications.
• Presents ongoing work and findings to colleagues both locally and at academic conferences.
• Communicates results/findings to the relevant scientific community via published papers.
• Develops and applies novel research methodology to investigate and perform feature extraction in medical imaging, molecular, and clinical data for predicting patient outcomes that will be translated into patient care.
• Builds innovative artificial intelligence (AI) solutions using the latest and advanced algorithms for improved patient care.
• Implements methods and algorithms for the analysis of complex multi-omics data sets including whole exome sequencing (WES), whole genome sequencing (WGS), targeted capture sequencing, RNA-seq, single-cell RNA-seq, ChIP-seq, Methylation sequencing, miRNA-seq, metabolomic, and proteomic data.
• Makes use of publicly available biological data including TCGA, GEO, TCIA, cbioportal, etc.
• Analyzes rare genetic variants for familial diseases to identify pathogenic variants of likely therapeutic intervention.
• Writes progress reports and research grant proposals for funding agencies.
• Adheres to standards as they appear in the Code of Conduct and Conflict of Interest policies.
• Adheres to and promotes Values.
  In view of the evolving needs and opportunities within the company, this position may be required to perform other duties as assigned and reporting relationships may vary.

Qualifications
ESSENTIAL

• Education: PhD in Bioinformatics
  PREFERRED
• Experience: Successful completion of Post-Doc fellowship

Job Specific Skills and Abilities

• Knowledge of scientific research methods and evaluation of research results.
• Effective problem-solving and analytical skills and ability to be detail-oriented.
• Strong background in multi-omics data analysis, including WES, WGS, RNA-seq, single-cell RNA-seq, ChIP-seq, and proteomics.
• Experience handling and analyzing large-scale biological datasets from public repositories (e.g., TCGA, GEO, TCIA, cBioPortal).
• Competence in statistical analysis and data visualization using programming languages such as Python and R.
• Familiarity with artificial intelligence (AI) and machine learning methods for clinical prediction and data integration.
• Ability to develop and implement novel algorithms and pipelines for feature extraction and predictive modeling in medical imaging and multi-modal clinical data.
• Prior experience in rare disease genetics and identification of pathogenic variants in familial cohorts.
• Strong publication record or demonstrable ability to draft manuscripts for peer-reviewed scientific journals.
• Strong knowledge of statistical methods and tools.
• Ability to assist in the management of a project, overcome obstacles, and meet deliverable deadlines.
• Proficiency with Microsoft Office suite.
• Fluency in written and spoken English.

Experience in one or more of the following disease areas:

• Nephrology (e.g., congenital or genetic kidney disorders)
• Cardiovascular disease (e.g., inherited or structural heart conditions)
• Neurological disorders (e.g., developmental or degenerative syndromes)
• Congenital malformations.
  Prior work may include disease modeling, biomarker discovery, genomic characterization, or translational research aimed at understanding pathophysiology and improving clinical outcomes.