We are seeking a highly analytical and innovative Bioinformatics Data Scientist to join our interdisciplinary team. In this role, you will integrate advanced computational methods, machine learning, and bioinformatics techniques to analyze complex biological datasets and extract actionable insights. Your work will directly contribute to the development of [precision medicine, novel therapeutics, diagnostics, or agricultural biotechnology solutions].

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Key Responsibilities:
Design and implement computational workflows for processing and analyzing large-scale omics data (e.g., genomics, transcriptomics, proteomics, epigenomics).
Apply statistical modeling and machine learning to uncover patterns, biomarkers, or therapeutic targets.
Integrate data from multiple sources (e.g., public datasets, clinical data, lab experiments) to generate comprehensive biological insights.
Develop visualization dashboards and reports to communicate findings to technical and non-technical stakeholders.
Collaborate with biologists, clinicians, and software engineers to support ongoing research and product development.
Contribute to publications, patents, and regulatory documentation, as applicable.
Ensure reproducibility, scalability, and maintainability of all developed code and pipelines.

Qualifications:

Masters or Ph.D. in Bioinformatics, Computational Biology, Data Science, Computer Science, or a related field.
[25+] years of experience working with biological datasets in an industry or academic setting.
Strong programming skills in Python and/or R; familiarity with data science libraries (e.g., pandas, scikit-learn, TensorFlow, ggplot2).
Experience working with high-throughput sequencing data (e.g., RNA-Seq, WGS, scRNA-Seq) and tools (e.g., GATK, DESeq2, STAR, Seurat).
Proficiency in statistics, machine learning, and data visualization.
Experience with version control (Git), cloud computing (AWS, GCP), and workflow tools (e.g., Nextflow, Snakemake).

Preferred Skills:

Experience with AI/ML approaches for biomarker discovery or drug response prediction.
Familiarity with relational databases and APIs for biomedical data (e.g., TCGA, ENCODE, GEO, EGA).
Background in molecular biology or translational research is a plus.
Experience in clinical bioinformatics or regulatory environments (e.g., FDA, HIPAA-compliant systems).