What Is a Career Health Score? How We Rate Every Occupation
Learn how RankMyCareer calculates the Career Health Score — a composite rating based on salary, growth, AI risk, stability, work-life balance, and skill demand.
Choosing a career is one of the highest-stakes decisions you will ever make, yet most people make it with incomplete information. Job listings tell you what a role pays today, but they say nothing about whether that salary will grow, whether the occupation is shrinking, or whether AI will fundamentally change the work within a decade. That is why we built the Career Health Score: a single, transparent metric that evaluates every occupation across the dimensions that actually determine long-term career viability.
The Career Health Score is a composite rating from 0 to 100 that combines data from the Bureau of Labor Statistics, O*NET, and leading academic AI research. It is designed to answer a simple but critical question: How healthy is this career as a long-term bet? Here is exactly how it works.
The Six Dimensions
Every Career Health Score is calculated from six weighted dimensions. Each dimension captures a distinct aspect of career viability, and together they provide a holistic assessment that no single statistic can offer.
| Dimension | Weight | What It Measures | Data Source |
|---|---|---|---|
| Salary | 25% | Median compensation and wage growth trajectory | BLS Occupational Employment and Wage Statistics |
| Job Growth Outlook | 20% | Projected employment growth rate and annual openings | BLS Employment Projections |
| AI Automation Resilience | 20% | Susceptibility to AI automation vs. augmentation | Felten et al., Eloundou et al., Frey & Osborne research |
| Job Stability | 15% | Employment security: layoff rates, quit rates, openings | BLS Job Openings and Labor Turnover Survey (JOLTS) |
| Work-Life Balance | 10% | Schedule flexibility, stress, hours, remote availability | O*NET Work Context data |
| Skill Demand | 10% | Transferability and cross-industry demand for core skills | O*NET Skills and Knowledge data |
1. Salary (Weight: 25%)
Compensation is the most tangible measure of a career's value. We evaluate both the current median salary and the trajectory of wage growth over the past five years. A career with a high median salary that has been stagnant scores lower than one with a slightly lower median that is growing rapidly. We use BLS Occupational Employment and Wage Statistics data, which covers over 800 detailed occupations across every industry.
For example, financial managers score exceptionally well on salary with a median exceeding $139,000 and consistent year-over-year growth. In contrast, roles like cashiers score low on this dimension due to both modest median pay and limited wage growth.
| Salary Scorer | Career | Median Salary | Wage Trend |
|---|---|---|---|
| High | Financial Managers | $139,000+ | Consistent growth |
| Low | Cashiers | Modest | Limited growth |
2. Job Growth Outlook (Weight: 20%)
A high salary means little if the occupation is contracting. We use BLS ten-year employment projections to assess whether an occupation is growing, stable, or declining. Careers with projected growth rates above 10 percent receive the highest scores, while those with negative growth are penalized. We also factor in the absolute number of projected annual openings, because a high growth rate in a tiny occupation is less meaningful than moderate growth in a massive one.
Nurse practitioners exemplify top-tier growth scores with over 40 percent projected growth through 2032 and thousands of annual openings. On the other end, data entry keyers have negative growth projections, reflecting structural decline driven by automation.
| Growth Scorer | Career | Projected Growth | Annual Openings |
|---|---|---|---|
| High | Nurse Practitioners | 40%+ | Thousands |
| Low | Data Entry Keyers | Negative | Declining |
3. AI Automation Resilience (Weight: 20%)
This is the dimension that makes RankMyCareer unique. We calculate AI exposure using a composite of three respected academic frameworks: the Felten et al. (2021) AI Occupational Exposure scores, which measure how much a job's tasks overlap with current AI capabilities; the Eloundou et al. (2023) GPT exposure analysis, which evaluates large language model applicability to specific work activities; and the Frey and Osborne (2017) automation probability estimates, which assess overall susceptibility to computerization.
Crucially, we distinguish between augmentation and automation. A career where AI makes workers more productive scores differently from one where AI replaces workers entirely. For instance, software developers have moderate AI exposure because AI coding tools augment their work, but the net impact is positive since developers become more productive rather than redundant. Conversely, telemarketers face high automation risk because AI voice systems can replicate the core task of outbound calling.
For every career in our database, we break down AI impact into three categories: tasks that are augmented by AI, tasks that are automated by AI, and tasks that remain safe from AI. This granular view helps you understand not just whether AI affects your career, but exactly how.
| AI Resilience | Career | AI Impact Type | Net Effect |
|---|---|---|---|
| High | Software Developers | Augmentation | Positive -- increased productivity |
| Low | Telemarketers | Automation | Negative -- core tasks replaceable |
4. Job Stability (Weight: 15%)
Job stability measures how secure your employment is once you enter a field. We draw on data from the BLS Job Openings and Labor Turnover Survey (JOLTS), which tracks quit rates, layoff rates, and job openings by industry. Careers in sectors with low layoff rates and high job openings receive higher stability scores.
Healthcare careers consistently score highest on stability. Registered nurses, physician assistants, and physical therapists operate in a sector with near-zero involuntary unemployment. Skilled trades like electricians and industrial machinery mechanics also score well due to persistent labor shortages.
| Stability Scorer | Career | Sector | Why It Scores Well |
|---|---|---|---|
| High | Registered Nurses | Healthcare | Near-zero involuntary unemployment |
| High | Electricians | Skilled Trades | Persistent labor shortages |
5. Work-Life Balance (Weight: 10%)
We evaluate work-life balance using O*NET work context data, which includes metrics on schedule flexibility, physical demands, stress levels, typical work hours, and remote work availability. Careers that offer regular schedules, moderate stress, and remote or hybrid options score higher on this dimension.
This dimension intentionally carries less weight at 10% because work-life balance is highly subjective. Some professionals prioritize flexibility above all else, while others willingly accept demanding schedules for higher compensation. The score provides a baseline comparison, but individual preferences should guide the final decision.
6. Skill Demand (Weight: 10%)
Skill demand measures how transferable and in-demand the core skills of an occupation are across the broader economy. We use O*NET skill and knowledge data to assess whether a career builds capabilities that are valued in multiple industries and roles. Careers that develop highly transferable skills like data analysis, project management, and technical writing score higher because they provide more career mobility if you ever need to pivot.
Management analysts score well on skill demand because their core competencies, including strategic thinking, data analysis, and organizational design, are valued across every industry. Highly specialized roles with narrow skill sets may score lower on this dimension even if they score well on salary.
How the Score Is Calculated
Each dimension is scored on a 0 to 100 scale based on where the occupation falls relative to all other occupations in our database. The final Career Health Score is a weighted average of all six dimension scores using the weights listed above. This produces a single number that balances compensation, growth, resilience, stability, quality of life, and career flexibility.
We deliberately chose not to use a single-factor ranking because no single metric tells the whole story. A career with the highest salary might have terrible growth prospects. A fast-growing occupation might be highly vulnerable to AI. The composite score captures trade-offs that single-metric rankings miss.
What the Scores Mean in Practice
| Score Range | Rating | Description | Example Careers |
|---|---|---|---|
| 80 - 100 | Exceptional | Excels across nearly every dimension. Strong pay, robust growth, and low automation risk. | Nurse Practitioners, Information Security Analysts, Software Developers |
| 60 - 79 | Strong | Solid bets with particular strengths in two or three dimensions. | Most skilled trades, mid-level healthcare roles, established business professions |
| 40 - 59 | Mixed | Notable strengths but also significant concerns such as stagnant wages, high AI exposure, or declining demand. | Careers requiring careful evaluation of personal priorities |
| Below 40 | Caution | Faces headwinds on multiple fronts. Workers should explore upskilling or career transitions. | Occupations with negative growth, high automation risk, and limited wages |
Limitations and Transparency
No scoring system is perfect, and we believe transparency about our methodology is essential. Here are the key limitations to keep in mind:
- Geographic variation. BLS data represents national medians. Salaries, growth rates, and job availability can vary dramatically by region. A career that scores modestly nationwide might be exceptional in your local market.
- Individual fit. The Career Health Score measures market-level career viability, not personal fit. A high-scoring career that does not align with your skills, interests, or values is not the right career for you.
- AI projections are estimates. Our AI automation resilience scores are based on the best available academic research, but the actual pace and direction of AI development is inherently uncertain. We update our models regularly as new research is published.
- Historical data. Salary and employment trends are based on historical BLS data. Past trends do not guarantee future outcomes, though they are the best available predictors.
How to Use the Score
The Career Health Score is designed to be a starting point, not an endpoint. Here is how we recommend using it:
- Start with your interests. Identify the career categories that align with your skills and passions. Do not chase a high score in a field you have no interest in.
- Compare within your area of interest. Use the score to compare related careers side-by-side. If you are drawn to healthcare, compare nurse practitioners against respiratory therapists against dental hygienists to see where the trade-offs lie.
- Dig into the dimension breakdown. The overall score is useful for quick comparisons, but the real insight comes from examining each dimension individually. A career with a moderate overall score might have a stellar growth outlook that matters more to you than a perfect salary score.
- Check the AI task breakdown. For any career you are seriously considering, read through the detailed AI impact analysis. Understanding which specific tasks are augmented, automated, or safe gives you actionable intelligence about where to focus your skill development.
The Bottom Line
The Career Health Score exists because we believe career decisions deserve the same rigor that investors apply to financial decisions. Your career is your most valuable asset, and understanding its long-term health across multiple dimensions is essential for making smart choices. Every score on RankMyCareer is backed by public data from authoritative sources, and our methodology is designed to be transparent and reproducible.
Explore the full Career Health Score for any of the 500+ occupations in our database. Compare careers side-by-side, filter by the dimensions that matter most to you, and use the data to make confident, informed decisions about your professional future.