Our Customer is looking to hire a Data Scientist with a background in time series sensor data analysis, supervised and unsupervised machine learning and at least some background in Bayesian statistics or Bayesian probability.
About the role
If you have worked on e.g. only time series analysis and some Bayesian stats with a coding background then that would be ok. Main duties would be, depending on the candidate, either assisting with activity detection or with analysis of user behaviour patterns for disease detection.
- Computer science, applied mathematics or similar technical background (e.g. physics).
- Experience writing code for data applications. Python is essential (but they do not need to be at a super high level as a coder).
- Good understanding / knowledge of Bayesian statistics.
Would also be nice if you have…
- C/C++ proficiency.
- Proficiency with SQL and/or NoSQL databases.
- Time series statistical model analysis (e.g. ARMA/ARIMA and related).
- Machine learning time series analysis (for example Hidden Markov Models or Recurrent Neural Networks).
- Experience with modern deep learning techniques including transformer networks.
- Digital signal processing experience e.g. fourier transforms.
Please do not apply if you…
- Are a generic deep learning or machine learning person, although it would depend on what you have worked on in the past. E.g. image classification is a big part of deep learning but not very useful for this role.
- Have Natural Language Processing as your focus.
- Are a generic statistician.
- Are a pure mathematician, or an applied mathematician without coding experience.
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