Where a data scientist can go next
12 destinations recorded, ranked by how much of the target role a data scientist already meets. The closer to the top of Table 1, the less there is to learn.
Data scientists find and interpret rich data sources, manage large amounts of data, merge data sources, ensure consistency of data-sets, and create visualisations to aid in understanding data. They build mathematical models using data, present and communicate data insights and findings to specialists and scientists in their team and if required, to a non-expert audience, and recommend ways to apply the data.
- Highest coverage recorded
- 80%
- Destinations
- 12each with a full record
- Within reach
- 12difficulty under 55
- Skills on file
- 97required or supplementary
Table 1 · Within reach
- data analyst 80% covered · difficulty 32.3
- demographer 72% covered · difficulty 41.2
- biometrician 69% covered · difficulty 45
- religion scientific researcher 69% covered · difficulty 40.7
- statistician 68% covered · difficulty 47.8
- astronomer 67% covered · difficulty 45.4
- data quality specialist 66% covered · difficulty 39.3
- seismologist 66% covered · difficulty 48.3
- philosopher 65% covered · difficulty 46.2
- meteorologist 65% covered · difficulty 47.6
- communication scientist 65% covered · difficulty 47.7
- computer scientist 64% covered · difficulty 52.3
Look up another occupation
data scientist is filed under Professionals, ISCO major group 2. The full index lists all ten groups.
How these figures were compiled
Coverage is the share of the target role’s weighted skill requirement a data scientist already meets, counting required skills in full and supplementary ones at 0.35. It is directional: the figure for the reverse move differs. Every entry above opens the full comparison for that move: what carries over, what is missing, and where to start. The full method.