Deep 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 are crucial and that’s where Deep Learning comes in. Deep Learning allows businesses to build precise models that they can use to identify opportunities to generate profit and avoid unknown risks.

Why consider a career as a Deep Learning Engineer?

Deep Learning is a subset of Deep Learning focused on artificial neural networks built on algorithms inspired by the function and structure of the brain. There is a large range of industries that utilize 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 Deep Learning Engineer
Deep Learning Analyst
Deep Learning Developer
Deep Learning Engineer
Deep Learning Researcher
Deep Learning Specialist
NLP & Deep Learning Scientist
NLP Software Engineer
Research Scientist – Deep Learning
Research Scientist – Image Analysis
Senior Computer Vision Researcher
Deep Learning Researcher

Career progression example

Graduate Deep Learning Researcher
Deep Learning Engineer

Salary Benchmarks

At an entry level, junior / graduate Deep 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.

Deep Learning skills: Supervised Learning | Unsupervised Learning | Input Layer | Hidden Layers | Output layer |  Neural Networks | Convolutional Neural Networks (CNN) | Recurrent Neural Networks (RNN) | Long/Short Term Memory (LSTM) | Markov Chain (MC) | Deep Convolutional Network (DCN).

There is a huge range of courses online that all you to improve your 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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