Translational Informatics Analyst information
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$67.6K is the 25th percentile. Wages below this are outliers.
$65K - $72.5K
20% of jobs
The median wage is $81.2K / yr.
$80K - $87.5K
14% of jobs
$97.3K is the 75th percentile. Wages above this are outliers.
$95K - $102.5K
19% of jobs
$102.5K - $110K
5% of jobs
$110K - $117.5K
3% of jobs
$117.5K - $125K
3% of jobs
How much do translational informatics analyst jobs pay per year?
As of Sep 11, 2026, the average yearly pay for translational informatics analyst in the United States is $85,609.00, according to ZipRecruiter salary data. Most workers in this role earn between $69,000.00 and $100,000.00 per year, depending on experience, location, and employer.
Translational informatics analysts are professionals who bridge the gap between biomedical research and clinical practice by managing, analyzing, and interpreting complex biological and clinical data. They work to translate research findings into actionable medical insights, supporting the development of new diagnostics, therapies, and personalized medicine approaches. These analysts often collaborate with researchers, clinicians, and IT specialists to integrate data from various sources and ensure its quality and usefulness in healthcare decision-making.
To thrive as a Translational Informatics Analyst, you need a background in bioinformatics, data analysis, and life sciences, often supported by an advanced degree in a related field. Expertise in programming languages (such as Python or R), experience with large-scale biological databases, and familiarity with data integration tools are typically required. Strong problem-solving skills, collaboration, and effective communication help bridge gaps between computational and clinical teams. These skills are crucial for translating complex biomedical data into actionable insights that advance research and patient care.
Translational Informatics Analysts often encounter challenges related to data standardization and interoperability when working with clinical and research datasets. These datasets can come from multiple sources with varying formats, quality, and privacy requirements, making harmonization complex. Overcoming these obstacles requires strong collaboration with clinicians, researchers, and IT teams to ensure data is accurately mapped, de-identified, and usable for downstream analysis. Staying current with evolving data governance regulations and leveraging robust data integration tools are also key aspects of the role.
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