The table below provides summary statistics and salary benchmarking for jobs advertised in St Neots requiring Python skills. It covers permanent job vacancies from the 6 months leading up to 5 March 2026, with comparisons to the same periods in the previous two years.
|
|
6 months to
5 Mar 2026 |
Same period 2025 |
Same period 2024 |
| Rank |
6 |
- |
- |
| Rank change year-on-year |
- |
- |
- |
| Permanent jobs citing Python |
1 |
0 |
0 |
| As % of all permanent jobs in St Neots |
4.17% |
- |
- |
| As % of the Programming Languages category |
100.00% |
- |
- |
| Number of salaries quoted |
1 |
0 |
0 |
| 10th Percentile |
- |
- |
- |
| 25th Percentile |
£35,000 |
- |
- |
| Median annual salary (50th Percentile) |
£40,000 |
- |
- |
| 75th Percentile |
£45,000 |
- |
- |
| 90th Percentile |
- |
- |
- |
| Cambridgeshire median annual salary |
£50,000 |
£70,000 |
£65,000 |
| % change year-on-year |
-28.57% |
+7.69% |
- |
Python falls under the Programming Languages category. For comparison with the information above, the following table provides summary statistics for all permanent job vacancies requiring coding skills in St Neots.
| Permanent vacancies with a requirement for coding skills |
1 |
0 |
0 |
| As % of all permanent jobs advertised in St Neots |
4.17% |
- |
- |
| Number of salaries quoted |
1 |
0 |
0 |
| 10th Percentile |
- |
- |
- |
| 25th Percentile |
£35,000 |
- |
- |
| Median annual salary (50th Percentile) |
£40,000 |
- |
- |
| 75th Percentile |
£45,000 |
- |
- |
| 90th Percentile |
- |
- |
- |
| Cambridgeshire median annual salary |
£60,000 |
£55,000 |
£60,000 |
| % change year-on-year |
+9.09% |
-8.33% |
- |
Python
Job Vacancy Trend in St Neots
Historical trend showing the proportion of permanent IT job postings citing Python relative to all permanent IT jobs advertised in St Neots.
Python
Salary Trend in St Neots
Salary distribution trend for jobs in St Neots citing Python.
Python
Co-Occurring Skills & Capabilities in St Neots by Category
The following tables expand on the one above by listing co-occurrences grouped by category. They cover the same employment type, locality and period, with up to 20 co-occurrences shown in each category:
