Data scientist to biometrician
A data scientist already meets 69% of what the biometrician role asks for. The move turns on 8 required skills not yet in the profile.
45/ 100
1 How much of what a biometrician is expected to do is already part of a data scientist’s work. Directional: the figure for the reverse move differs, because moving into a speciality is not the same problem as moving out of one. 2 Every skill the biometrician role is recorded as needing, required and optional together. What these numbers mean.
Why this move works
A biometrician role treats 36 of its required skills as things a data scientist already does. You would walk in with these on day one.
- apply for research funding
- apply research ethics and scientific integrity principles in research activities
- communicate with a non-scientific audience
- computational biology
- conduct research across disciplines
- data science
What stands in the way is 8 required skills the profile does not yet cover. Table 3 groups them; Table 4 says which to take first.
Table 2 · What you already bring
Of the 40 skills that carry over, these are the ones fewest other occupations ask for. A biometrician role needs them, and most people applying for one will not have them already. This is the part of a data scientist background worth leading with.
- already held statistical modeling techniques natural sciences, mathematics and statistics
- already held computational biology natural sciences, mathematics and statistics
- already held interpret current data information skills
- already held manage data collection systems information skills
- already held data science information and communication technologies (icts)
- already held multidisciplinary research generic programmes and qualifications
All 40 carried skills, including the 36 the biometrician role treats as required.
Table 3 · What you may need to learn
The 33 missing skills fall into 8 areas of the ESCO skill hierarchy, numbered in the order worth working in. Areas carrying the most required skills come first, and inside each one the required skills sit above the optional ones. Nothing here is a verdict on you: it is a list of what this particular job asks for that your current one does not.
- still to learn biometrics required
- still to learn life sciences required
- still to learn mathematics required
- optional, not held computational chemistry optional
- optional, not held proteomics optional
- optional, not held stem cells optional
- optional, not held biology optional
- optional, not held statistical analysis system software optional
- still to learn plan research process required
- still to learn apply statistical analysis techniques required
- still to learn develop scientific research protocols required
- optional, not held develop scientific theories optional
- optional, not held conduct public surveys optional
- optional, not held gather experimental data optional
- optional, not held manage database optional
- still to learn scientific modelling required
- optional, not held SAS language optional
- optional, not held screen reader optional
- still to learn scientific research methodology required
- optional, not held contribute to development of biometric systems optional
- optional, not held create software design optional
- optional, not held advise on legislative acts optional
- optional, not held apply teaching strategies optional
- optional, not held assist in clinical trials optional
- optional, not held assist scientific research optional
- optional, not held prepare lesson content optional
- optional, not held provide lesson materials optional
- optional, not held write research proposals optional
- optional, not held write work-related reports optional
- optional, not held develop statistical software optional
- optional, not held prepare visual data optional
2 further areas in the appendix
Table 4 · Where to start
The 3 entries a biometrician role is least likely to hire without. The ordering is computed from the skill data, not from what pays.
- 01 plan research process practise this
- 02 apply statistical analysis techniques practise this
- 03 biometrics study this
Each entry opens a course search for that skill. Career Overlap earns nothing from these links.
Appendix · The rest of the record
All 40 skills that carry over
- already held apply for research funding skill
- already held apply research ethics and scientific integrity principles in research activities skill
- already held communicate with a non-scientific audience skill
- already held computational biology knowledge
- already held conduct research across disciplines skill
- already held data science knowledge
- already held demonstrate disciplinary expertise skill
- already held develop professional network with researchers and scientists skill
- already held disseminate results to the scientific community skill
- already held draft scientific or academic papers and technical documentation skill
- already held evaluate research activities skill
- already held execute analytical mathematical calculations skill
- already held increase the impact of science on policy and society skill
- already held integrate gender dimension in research skill
- already held interact professionally in research and professional environments skill
- already held interpret current data skill
- already held manage findable accessible interoperable and reusable data skill
- already held manage intellectual property rights skill
- already held manage open publications skill
- already held manage personal professional development skill
- already held manage research data skill
- already held mentor individuals skill
- already held multidisciplinary research knowledge
- already held operate open source software skill
- already held perform project management skill
- already held perform scientific research skill
- already held promote open innovation in research skill
- already held promote the participation of citizens in scientific and research activities skill
- already held promote the transfer of knowledge skill
- already held publish academic research skill
- already held research design knowledge
- already held speak different languages skill
- already held statistics knowledge
- already held synthesise information skill
- already held think abstractly skill
- already held write scientific publications skill
- already held apply blended learning skill
- already held manage data collection systems skill
- already held statistical modeling techniques knowledge
- already held teach in academic or vocational contexts skill
The 2 learning areas not shown above
- optional, not held assess environmental impact optional
- optional, not held prepare exercise session optional
25 supplementary skills, helpful but not required
- optional, not held contribute to development of biometric systems optional
- optional, not held SAS language optional
- optional, not held computational chemistry optional
- optional, not held create software design optional
- optional, not held develop scientific theories optional
- optional, not held prepare exercise session optional
- optional, not held proteomics optional
- optional, not held stem cells optional
- optional, not held advise on legislative acts optional
- optional, not held apply teaching strategies optional
- optional, not held assess environmental impact optional
- optional, not held assist in clinical trials optional
- optional, not held assist scientific research optional
- optional, not held biology optional
- optional, not held conduct public surveys optional
- optional, not held develop statistical software optional
- optional, not held gather experimental data optional
- optional, not held manage database optional
- optional, not held prepare lesson content optional
- optional, not held prepare visual data optional
- optional, not held provide lesson materials optional
- optional, not held screen reader optional
- optional, not held statistical analysis system software optional
- optional, not held write research proposals optional
- optional, not held write work-related reports optional
57 held skills the biometrician role does not ask for
- not needed by the target role Hadoop knowledge
- not needed by the target role LDAP knowledge
- not needed by the target role LINQ knowledge
- not needed by the target role MDX knowledge
- not needed by the target role N1QL knowledge
- not needed by the target role SPARQL knowledge
- not needed by the target role XQuery knowledge
- not needed by the target role build recommender systems skill
- not needed by the target role business analytics knowledge
- not needed by the target role business intelligence knowledge
- not needed by the target role collect ICT data skill
- not needed by the target role computer simulation knowledge
- not needed by the target role create data models skill
- not needed by the target role data engineering knowledge
- not needed by the target role data ethics knowledge
- not needed by the target role data mining knowledge
- not needed by the target role data models knowledge
- not needed by the target role data quality assessment knowledge
- not needed by the target role data visualisation software knowledge
- not needed by the target role define data quality criteria skill
- not needed by the target role deliver visual presentation of data skill
- not needed by the target role design database in the cloud skill
- not needed by the target role design database scheme skill
- not needed by the target role develop data processing applications skill
- not needed by the target role digital curation knowledge
- not needed by the target role empirical analysis knowledge
- not needed by the target role establish data processes skill
- not needed by the target role handle data samples skill
- not needed by the target role healthcare analytics knowledge
- not needed by the target role image recognition knowledge
- not needed by the target role implement data quality processes skill
- not needed by the target role information categorisation knowledge
- not needed by the target role information extraction knowledge
- not needed by the target role integrate ICT data skill
- not needed by the target role make data-driven decisions skill
- not needed by the target role manage ICT data architecture skill
- not needed by the target role manage ICT data classification skill
- not needed by the target role manage data skill
- not needed by the target role marketing analytics knowledge
- not needed by the target role mathematical modelling knowledge
17 further entries not listed here
12 gaps that are knowledge rather than practice
Knowledge gaps usually close through study. Practical skill gaps usually need something you can point at.
- still to learn biometrics required
- still to learn life sciences required
- still to learn mathematics required
- still to learn scientific modelling required
- still to learn scientific research methodology required
- optional, not held SAS language optional
- optional, not held computational chemistry optional
- optional, not held proteomics optional
- optional, not held stem cells optional
- optional, not held biology optional
- optional, not held screen reader optional
- optional, not held statistical analysis system software optional
Other moves recorded from data scientist
- data analyst 80% covered · moderate
- demographer 72% covered · moderate
- religion scientific researcher 69% covered · moderate
- statistician 68% covered · moderate
- astronomer 67% covered · moderate
- data quality specialist 66% covered · moderate
- seismologist 66% covered · moderate
- philosopher 65% covered · moderate
Common questions about this move
Can a data scientist become a biometrician?
Yes, and it is a moderate move. A data scientist already covers 69% of what a biometrician is expected to do: 40 skills carry over, 36 of which the biometrician role treats as required.
What skills does a data scientist need to become a biometrician?
8 required skills are missing, plus 25 optional ones. They fall into 8 areas of the ESCO skill hierarchy, led by natural sciences, mathematics and statistics. The single most important is plan research process.
How hard is the move from data scientist to biometrician?
Its learning distance is 45 out of 100, which is the moderate band. That accounts for both how much is missing and how specialised those skills are.
How this record was compiled
Both occupations are taken from ESCO, which lists the skills and knowledge each occupation is expected to have and marks every one required or optional. Nothing here is a prediction about hiring, and nothing here knows that a particular employer wants a particular certificate. Treat Table 3 as a starting point for your own research rather than a syllabus. The full method states what these figures can and cannot tell you.