Research & Development (R&D) Lead

Role Summary
We are seeking a PhD-level Bioinformatics R&D Lead to define and drive our applied-research agenda at the intersection of bioinformatics, computational biology, and AI / machine learning. This person will lead research initiatives from concept to prototype to production — developing and validating computational methods for genomic and diagnostic data — and coordinate closely with our engineering teams to translate them into clinical-grade tools. This is a hands-on leadership role that blends scientific rigor with practical delivery, open to exceptional candidates globally.
Key Responsibilities
Define and own the bioinformatics R&D roadmap, aligning research priorities with the company's diagnostic products and business goals.
Lead applied research combining bioinformatics / computational biology with machine learning and AI — e.g., NGS/genomic data analysis, variant interpretation, and predictive modeling for diagnostics.
Design, build, and validate reproducible bioinformatics pipelines and ML models on genomic, multi-omic, molecular, and clinical datasets.
Lead and coordinate a multidisciplinary team — bioinformaticians, computational biologists, data scientists, and software / DevOps engineers — delegating and assigning work across domains, including coding and engineering tasks.
Rapidly prototype, benchmark, and move promising research into production alongside engineering teams.
Contribute to publications, patent applications, and regulatory / clinical submissions where applicable.
Mentor and technically guide developers and junior researchers, fostering a culture of experimentation and scientific rigor.
Stay ahead of academic and industry developments in genomics and AI, and represent the company's technical thought leadership.
Ensure research and data practices meet relevant healthcare data privacy, quality, and regulatory requirements.
Required Qualifications
PhD in Bioinformatics, Computational Biology, Genomics, Systems Biology, Biostatistics, Computer Science, or a related quantitative field — with demonstrated work at the intersection of biology/medicine and computation.
Minimum 3 years of post-degree experience applying computational methods to biological / genomic data (industry experience in biotech, diagnostics, or pharma preferred).
Ability to work independently, drive initiatives with minimal supervision, and delegate and assign work across other domains (e.g., coding and engineering tasks) to the relevant teams.
Strong machine learning / AI skills with hands-on experience in modern frameworks (e.g., PyTorch, TensorFlow, scikit-learn) applied to genomics or biomedical data.
Strong programming in Python and R, with experience using bioinformatics workflow tools (e.g., Nextflow, Snakemake, or WDL/Cromwell) in Linux environments.
Hands-on experience analyzing large, multidimensional genomics datasets (NGS, RNA-Seq, transcriptomics, and similar).
Demonstrated research excellence (publications, patents, or a strong applied-research portfolio), and a proven ability to translate research into production-grade systems and lead cross-functional teams.
Preferred Qualifications
Domain experience in oncology / cancer genomics, hematology, molecular diagnostics, or multi-omics integration.
Experience with cloud platforms (AWS/GCP/Azure) and MLOps for scalable, reproducible analysis.
Familiarity with clinical and regulatory frameworks (e.g., CLIA/CAP, FDA submissions, HIPAA / data privacy) and assay validation.
Familiarity with healthcare data standards (e.g., EHR, HL7/FHIR).
Prior experience building, leading, or mentoring a bioinformatics / computational biology team.

Work arrangement
Yes