Data scientist’s salary
One of the most frequent question I get asked is, what salary can a data scientist (with 5 years experience) expect.
I belong to a category of data scientist that has a relevant PhD, post-doc and multi-domain experience. With this in mind, I can only address this question for my type or category of data scientists. Also, this information is only relevant to Australia and utmost APAC region. I am sure that the numbers will vary depending on the region.
Australia is fairly small in terms of economy, innovation and science & technology in general. However, there is a need for data scientist in variety of domains here. I will try and cover most of them one by one. I will be covering the domains that demands/expects/requires the category of data scientists that I belong to, according to my observation. There could be other industries and/or domains that demands data scientists that may not be listed below. If you know of any other industry and/or domain, that is not listed below but has demand for data scientists, then please do let me know. Further information will help enrich this blog post.
The salary, I estimate here, includes all bonuses and perks and excludes superannuation.
- Publicly funded Science & Technology (includes research institutions, government agencies, etc):
Salary ranges from $110K – $140K
- Medium size – Industry Science & Technology (includes industry players similar to Canon, etc):
Salary ranges from $120K – $160K
- Large size – Industry Science & Technology (includes likes of Google, Amazon, etc):
Salary ranges from $150 – $220K
- Fast-moving Consumer Goods (FMCG):
Salary ranges from $120- $160K
Salary ranges from $125 – $160K
- Banking & Financial Services:
Salary ranges from $120 – $160K
- Far from money – Financial Markets:
Salary ranges from $160 – $230K
- Close to money – Financial Markets:
Salary ranges from $180 – $300+K
The ranges presented here are approximate. The final figure will depend upon candidate’s negotiating capacity, soft skills, specific skillset/experience, and the employer’s needs/fancies.
Disclaimer: These are my observation of salaries. The reader should note that there is a probability that my observations might be wrong and/or uninformed.