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Machine Learning jobs and careers

As the use of data in business gets more and more prevalent, the ability to quickly and effectively produce data models that can analyse large complex datasets is crucial and that’s where Machine Learning comes in. Machine Learning allows businesses to build precise models that they can use to identify opportunities to generate profit and avoid unknown risks. Deep Learning is a subset of Machine Learning, focused on artificial neural networks built on algorithms inspired by the function and structure of the brain.

Why consider a career as a Machine Learning Engineer?

There is a large range of industries that utilise Machine Learning such as; Financial Services, Healthcare, Government, Marketing and Sales, Oil and Gas and Transport.

Typical role titles include

Algorithm Researcher
Computer Vision Engineer
Computer Vision Expert
Computer Vision Scientist
Computer Vision Software Engineer
Graduate Machine Learning Engineer
Machine Learning Analyst
Machine Learning Developer
Machine Learning Engineer
Machine Learning Researcher
Machine Learning Specialist
ML Engineer
ML Engineer
ML Software Engineer
ML Specialist
NLP & Deep Learning Scientist
NLP Software Engineer
Research Scientist – Deep Learning
Research Scientist – ML & Image Analysis
Senior Computer Vision Researcher
Deep Learning Researcher

Career progression example

  • Graduate Machine Learning Researcher
  • Machine Learning Engineer
  • Senior ML Engineer
  • Head of ML (or similar)

Salary Benchmarks

At an entry level, junior / graduate Machine Learning professionals on average will be earning between £30k-£35k. At the mid-level you’d be looking to earn £50k-£60k and seniors would be looking to earn £70k+.

Is it right for me? The skills it takes…

The skills needed can be split into 3 categories; fundamental skills, programming skills and machine learning languages.

Fundamental skills: Probability – using techniques like Markov Decision Processes and Bayes Nets | Statistics – being able to analyse variance and test hypotheses | Data Modelling – understanding the structure of datasets, spotting gaps and filling them with data.

Programming skills: Fundamentals – Data Structures | Algorithms | Computer Architecture | b-trees | Sort Algos and Stacks | Software Design – A background in APIs e.g. web APIs and static/dynamic libraries | ML Libraries – e.g. TensorFlow, CNTK, MLib.

Machine Learning languages: C/C++ – e;g; LibSVM, Shark and mlpack | R | Python – NumPy, SciPy Pandas, scikit-learn, Theano and TensorFlow.

There are a huge range of courses online that all help to improve skills in these areas such as; Coursera, Udacity and Kaggle.

What qualifications does it take?

A Masters (MSc) or Doctorate (PhD) in a maths, physics, statistics, artificial intelligence (AI), software engineering, engineering or computer science field with technical skills in programming. Statistics and probability are key here as well as demonstrable experience working with large data sets and experience using machine learning and statistical methods.

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