About the job

HAYA deploys a combination of experimental and computational solutions for target discovery and drug development. We are expanding our computational projects, integrating primary genomic, transcriptomic and epigenomic data to develop new targets for disease. We are seeking a senior bioinformatician with solid experience in transcriptomics and epigenomics data analysis in the context of metabolic disease. As we heavily focus on the regulatory genome, the ideal candidate should be an expert with RNA-seq methods (bulk and single cell) in addition to multiple functional genomics approaches. Digging deeper, the candidate will be adept at fitting complex pieces of the epigenomic puzzle together to tell biological stories within an applied industrial setting.

Primary Responsibilities:

• Focused investigations on data profiled in-house or from public repositories to identify promising lncRNA targets dysregulated in various metabolic diseases
• Integration of multiple functional genomics data sources to understand how non-coding targets behave between cell states, and evaluation of target behavior after perturbation
• Driving the identification of non-coding transcripts and examining differential expression, to produce figures/reports revealing new targets
• Deployment of bioinformatic pipelines for the automatic processing of -omics data, in particular raw data from RNA-seq and functional genomics experiments

Qualifications:

• PhD in quantitative (e.g. computer science, bioinformatics) or life sciences with a solid understanding of metabolic disease (or 4+ years of industry experience)
• Strong acumen for combining data from multiple sources to tell a biological story
• Experience with non-coding RNA targets discovery and characterization, and transcriptomic NGS data analysis with a focus on the non-coding genome
• Demonstrated history of delivering single cell RNA/ATAC-seq analysis packages 
• Proficiency with the analysis of primary epigenomic data (e.g., ATAC-seq, PRO-seq, GRO-seq, CUT&RUN, chromatin conformation capture techniques) to identify functional regions within the regulatory genome is a plus
• Familiarity with RNA-seq pipelines (both bulk and single cell) for expression quantification
• Proven track record applying these techniques in metabolic disease settings and integrating complex datasets is a strong plus
• Proficiency in R or Python, with solid experience in Linux
• Experience with genome browser visualizations
• Excellent written and verbal communication skills

Beyond these primary tasks, you will be immersed in additional exciting projects, analyzing new cutting-edge molecular profiling techniques.