Principal Recruiter
Data Platform & Architecture
View profileAs 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
We’ve outlined the latest salary benchmarks for Deep Learning roles in the guides below:
Data, Insight and Analytics Salary Guide
Data, Insight and Analytics Day Rate Guide
Data, Insight and Analytics Within Data Engineering Development Salary Guide
Data, Insight and Analytics Within Data Science Salary Guide
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.
We connect ambitious organisations with their greatest assets, equally ambitious talent.
“From finding me my current role to helping place two fantastic hires, ADLIB has been a pivotal role in the development of our team here at Bristol. The guys are easy to work with, friendly and most importantly inspire trust. I look forward to working with them in the future.”
Working with a recruitment partner like ADLIB means you’re working with a group of people who get to understand you and match candidates up with your culture and goals. ADLIB worked at a breakneck speed to introduce us to the marketing experts we needed to build our team in a matter of months. With their help we recruited and onboarded a new team much faster than planned and ended up spoilt for choice because of the way they work and market insights.
Average salaries and day rates typically received for Data, Insight and Analytics roles.
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Average salaries and day rates typically received for Data, Insight and Analytics roles.
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Average salaries and day rates for roles within data engineering & development.
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Average salaries and day rates typically received for Data, Insight and Analytics roles within Data Science.
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