Keywords to help you build a stand-out Data Scientist CV #
Crafting a Data Scientist CV or LinkedIn profile that is eye-catching or AI-catching (unfortunately, more recruiters are getting lazy and using the machine to filter out candidates) requires understanding your desired role and knowing what skills, experience, and certifications are in demand. This is where this post can help you! Using the power of scraping tools and AI, I have analysed 1000s of Credit Analyst job descriptions to identify in-demand qualifications. Overlay this with your relevant skills and experience to develop that winning CV, cover letter, or LinkedIn profile.
Analysis of Data Scientist Job Descriptions covers the following focus areas: #
- Skills
Skills desired for the role. - Proficiency
Skills that require a high level of competency and capability. - Experience
Areas of work experience sought by recruiters
Focus Area items are then categorized into High, Mid, and Low visibility #
The traits within the above areas are categorized into high, mid, or low visibility.
High indicates the relevant traits in this area that are highly visible in unique job postings. What can you do with them? These items are associated with a strong level of interest in hiring companies. Review and tweak your LinkedIn profile or CV to ensure you have emphasized these areas sufficiently.
Mid-category traits give you a view of broadly visible areas, while low-visibility traits tend to be specific to certain industries or categories of a role.
Data Scientist Skills #
Communication and presentation skills are highly sought after. Data scientists are often expected to be able to communicate and present complex technical information in a way that is meaningful and accessible to non-technical audiences. Having strong communication skills means data scientists can explain their work and results to all levels of an organization and help them to make informed decisions based on the data.
Programming and in particular, Python, rank highly as well. Programming concepts are important as statistical packages and databases require Data Scientists to write scripts to access, extract and perform calculations on the data. One of the more popular programming languages used for data wrangling, statistics, and machine learning is Python.
Machine learning also features highly in data scientist job ads.
What tools or areas should Data Scientists be proficient in? #
Data Scientists are expected to be proficient in data processing, analysis, and data science tools. They are expected to be able to extract and manipulate data into formats that can be easily analyzed and fed into data science tools. Experience in data visualisation and business intelligence tools is another area that is another common requirement.
| Job Title | Industry | Keywords | Visibility Index | Visibility | Focus Area | visible index |
|---|---|---|---|---|---|---|
| Data Scientist | All Industries | data processing, analysis, science tools | 59 | HIGH | Proficiency | 5,26 |
| Data Scientist | All Industries | data visualization tools | 58 | HIGH | Proficiency | 5,16 |
| Data Scientist | All Industries | Python | 37 | HIGH | Proficiency | 3,13 |
| Data Scientist | All Industries | programming languages | 9 | HIGH | Proficiency | 0,42 |
| Data Scientist | All Industries | software tools | 9 | HIGH | Proficiency | 0,42 |
| Data Scientist | All Industries | business intelligence tools | 7 | HIGH | Proficiency | 0,22 |
| Data Scientist | All Industries | SQL | 7 | HIGH | Proficiency | 0,22 |
| Data Scientist | All Industries | reporting tools | 6 | MID | Proficiency | 0,13 |
| Data Scientist | All Industries | source tools | 6 | MID | Proficiency | 0,13 |
| Data Scientist | All Industries | CD tools | 5 | MID | Proficiency | 0,03 |
Desired Experience of Data Scientists #
| Job Title | Industry | Keywords | Visibility Index | Visibility | Focus Area | visible index |
|---|---|---|---|---|---|---|
| Data Scientist | All Industries | data analysis | 37 | HIGH | Experience | 2,70 |
| Data Scientist | All Industries | data science | 33 | HIGH | Experience | 2,70 |
| Data Scientist | All Industries | Python | 25 | HIGH | Experience | 2,27 |
| Data Scientist | All Industries | customer | 23 | HIGH | Experience | 1,18 |
| Data Scientist | All Industries | industry | 23 | HIGH | Experience | 1,40 |
| Data Scientist | All Industries | software development | 17 | HIGH | Experience | 1,18 |
| Data Scientist | All Industries | data visualization | 15 | HIGH | Experience | 0,54 |
| Data Scientist | All Industries | machine | 14 | MID | Experience | 0,32 |
| Data Scientist | All Industries | SQL | 14 | MID | Experience | 0,21 |
| Data Scientist | All Industries | user | 14 | MID | Experience | 0,21 |
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