Explore safer careers (2)
Lower estimated automation risk
Why it fits
Uses algorithms, computational methods, research design, software prototypes, and complex datasets.
Why it fits
Fits scientists using disease biology, genomics, study design, clinical evidence, and publications.
Alternative careers
Related career paths that build on similar skills and experience
Why it fits
Uses biomedical data, clinical systems, terminology, privacy, decision support, and care-team data needs.
Why it fits
Applies clinical datasets, data quality, coding, protocols, database checks, and reporting.
Occupation snapshot
What does this snowflake show?
What's this?
We rate jobs using four factors. These are:
- Chance of being automated
- Job growth
- Wages
- Volume of available positions
These are some key things to think about when job hunting.
Risk & user votes
Calculated automation risk
Minimal Risk (0-20%): This occupation appears difficult to replace end-to-end with current or near-future automation, including AI software and robotics. Roles in this range usually depend on human judgement, creativity, care, leadership, specialist expertise, or adapting to messy real-world situations. AI and machines may still change parts of the work, but the occupation is likely to remain a distinct human role.
More information on what this score is, and how it is calculated is available here.
Human strengths important in this job
These are human abilities and work contexts that are important in this occupation. They may help explain why parts of the role are harder to replace end-to-end, but they are not the only inputs into the automation score.
Thinking creatively
Very importantWhy this matters
Decision-making and problem solving
Very importantWhy this matters
Communicating with people outside the organization
Very importantWhy this matters
Social perceptiveness
Quite importantWhy this matters
Coordinating others’ work
Quite importantWhy this matters
Show 3 more strengths
Developing objectives and strategies
Quite importantWhy this matters
Active learning
Quite importantWhy this matters
Education and training expertise
Quite importantWhy this matters
What users think
Based on 142 votes
Our visitors have voted they are unsure if this occupation will be automated. However, employees may be able to find reassurance in the automated risk level we have generated, which shows 19% chance of automation.
What do you think the risk of automation is?
What is the likelihood that Bioinformatics Scientists will be replaced by robots or artificial intelligence within the next 20 years?
Sentiment
Based on user votes over time
View sentiment trend
How opinions have changed over time
Pay & outlook
Wages
In 2024, the median annual wage for Biological Scientists, All Other was $93,330 ($45 per hour).
The median annual wage for Biological Scientists, All Other was 88.5% higher than the national median annual wage, which stood at $49,500.
Growth
The number of 'Biological Scientists, All Other' job openings is expected to rise 1.2% by 2034
Updated projections are due 09-2025.
Volume
As of 2024 there were 59,710 people employed as 'Biological Scientists, All Other' within the United States.
This represents around < 0.001% of the employed workforce across the country
Put another way, around 1 in 2 thousand people are employed as 'Biological Scientists, All Other'.
People also viewed
Job description
Conduct research using bioinformatics theory and methods in areas such as pharmaceuticals, medical technology, biotechnology, computational biology, proteomics, computer information science, biology and medical informatics. May design databases and develop algorithms for processing and analyzing genomic information, or other biological information.
O*NET-SOC code: 19-1029.01
What people are saying (5)
1) AI can process and analyze large amounts of data much faster than humans can. This is essential in bioinformatics, where scientists are often dealing with terabytes or petabytes of data. AI can help to identify patterns and correlations in this data that would be impossible for humans to find.
2)AI can be programmed to perform repetitive tasks with high accuracy. This is another important skill for bioinformatics scientists, who often spend a lot of time performing tasks such as data cleaning and analysis. AI can automate these tasks, freeing up scientists to focus on more creative and strategic work.
3)AI can be used to generate hypotheses and make predictions. This is a critical skill for bioinformatics scientists, who are constantly trying to understand the complex relationships between biological molecules. AI can be used to analyze data and identify patterns that may suggest new hypotheses or predictions.
4)AI can be used to design and conduct experiments. This is a more advanced skill, but it is one that AI is becoming increasingly capable of. AI can be used to design experiments that are more efficient and effective than those that could be designed by humans.
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