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We are seeking a lead bioinformatician to establish robust interpretative frameworks for novel NGS-based assays. A successful candidate will integrate with experimental and computational teams, collaborating on experimental design, data pipeline development, and conceptually lead and implement data analysis and interpretation. Ideal candidates will have a strong engineering mindset, excited to build analysis workflows from the ground up and enjoy the creative challenge of decoding information-rich datasets into actionable insights.
Job Responsibility:
Plan, develop, and execute reusable pipelines for processing NGS data
Benchmark and evaluate alternative data processing procedures
Conceptualize, design, and implement a robust interpretation framework
Identify and integrate multiomic data to inform data interpretation
Generate reports/dashboards for data QC metrics and interpretive readouts
Collaborate with experimentalists on experimental design and protocol optimization
Communicate results (oral, written) to colleagues, collaborators, and stakeholders
Manage and advise junior associates
Maintain knowledge of state-of-the-art methodologies and best practices
Requirements:
PhD in Bioinformatics, Computational Biology, Systems Biology, or related field
In-depth experience with NGS data processing (e.g., RNA-seq, ChIP/CLIP-seq, CRISPR screens, peak calling)
Fundamental understanding of mRNA lifecycle and associated experimental assays
Mastery of core statistical and ML procedures for normalization, differential analysis, dimensionality reduction, and clustering
Knowledge of statistical power analyses for experimental design
Strong interdisciplinary communication skills
Fluency in Unix and scripting languages (e.g., Python, R)
Experience with version-control and writing technical documentation
Nice to have:
Additional postdoctoral or industry experience
Complex (heterogenous) data integrations for functional insights
Factor analysis for signal decomposition (e.g., NMF, model or VAE-based methods)
DNN or other ML methodologies for sequence-to-profile (coverage, expression) modeling, with a focus on de novo feature identification
Specialized expertise in mRNA-protein interactions and associated assays
Pipeline development with a workflow language (e.g., Nextflow, Snakemake)