Remote Precision Medicine Data Lead for AI Training

Alignerr Corp.; Adelaide; On-site; AUD 152,000 - 248,000; Full time

Job summary

Alignerr is seeking a Precision Medicine Data Lead to shape how AI interprets genomic and clinical data for personalized medicine. This remote contract role focuses on designing end-to-end data pipelines and guiding AI training workflows with a strong data governance mindset.

You will collaborate across clinical, research, and engineering teams to ensure data quality, regulatory alignment, and scientific rigor while translating complex datasets into clinically actionable insights.

Qualifications

• Candidates should have a strong background in genomics and clinical analytics.
• Experience leading data integration initiatives in regulated environments is required.
• Ability to translate complex datasets into actionable insights for AI systems.

Responsibilities

• Design pipelines integrating genomic data with clinical records and biomarkers.
• Lead strategies to convert integrated datasets into precision medicine insights.
• Evaluate AI outputs for accuracy and clinical relevance; ensure data quality and governance.
• Collaborate with clinical, research, and engineering teams ensuring regulatory alignment.
• Communicate findings effectively to technical and non-technical stakeholders.
• Oversee data governance and ethical considerations in AI-trained models.

Skills

Genomics data

Data leadership

Data integration

Data governance

Cross-functional collaboration

Regulatory compliance

Education

MSc Genomics

PhD Bioinformatics

Tools

AI training workflows

EHR systems

Biobank datasets

Bioinformatics tools

Job description

Alignerr is seeking a Precision Medicine Data Lead to shape how AI interprets genomic and clinical data for personalized medicine. This remote contract role focuses on designing end-to-end data pipelines and guiding AI training workflows with a strong data governance mindset.

You will collaborate across clinical, research, and engineering teams to ensure data quality, regulatory alignment, and scientific rigor while translating complex datasets into clinically actionable insights.