Calculator inputs
Example data table
| Career | Salary | Bonus | Salary Growth | Interest Fit | Training Cost | Security |
|---|---|---|---|---|---|---|
| Data Analyst | $62,000 | $3,000 | 6% | 8.0 | $6,500 | 8.0 |
| UX Researcher | $70,000 | $4,500 | 5% | 9.0 | $12,000 | 7.0 |
| Product Manager | $90,000 | $10,000 | 7% | 7.0 | $18,000 | 8.0 |
Formula used
Starting compensation: Base Salary + Bonus
Projected earnings: Σ [Starting Compensation × (1 + Growth Rate)t] for each year in the horizon.
Discounted career value: Σ [After-Tax Compensationt / (1 + Discount Rate)t] − Training Cost
Normalized score: Positive factors use (Value − Min) / (Max − Min) × 100. Cost, stress, and training time use the inverse form.
Overall weighted score: Σ (Weight × Normalized Score) / Σ Weights
How to use this calculator
- Enter the comparison horizon, discount rate, and estimated tax rate.
- Add three career paths with pay, growth, fit, and training details.
- Score each qualitative factor on the 0 to 10 scale.
- Set importance weights so the calculator reflects your priorities.
- Click Compare career paths to rank the options instantly.
- Review the summary table, charts, payback period, and exported files.
Frequently asked questions
1. What does the weighted score mean?
It combines your importance weights with each career’s normalized performance. A higher score means that option matches your chosen priorities better than the others.
2. Why are some factors inverted?
Stress, education years, and training cost are better when lower. The calculator reverses those metrics so better outcomes still receive higher normalized scores.
3. Can I ignore a factor?
Yes. Set that factor’s weight to zero. It stays visible for reference but does not affect the final ranking.
4. What is discounted career value?
It estimates the present value of after-tax earnings over your chosen horizon, then subtracts upfront training cost. It helps compare paths with different timing and education needs.
5. Does this predict the future exactly?
No. It is a decision-support model. Better inputs improve usefulness, but real salaries, demand, and satisfaction can change over time.
6. How should I choose weights?
Start by giving your top priorities weights near 8 to 10. Use midrange values for secondary goals and zero for factors you do not want influencing the result.
7. What does training payback show?
It estimates how many months of after-tax annual compensation are needed to recover training cost. Shorter payback usually means lower upfront career switching risk.
8. Can I use more realistic local data?
Yes. Replace the sample values with your own salary research, tuition estimates, and self-ratings. The comparison becomes much more useful when inputs reflect your real situation.