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Machine Learning Scientist (Distributed Systems, Tensorflow) - Cambridge - November-04-2017 (FcARx)

@machinelearnbot

We are currently seeking a hands-on Machine Learning Scientist (Distributed Systems, Tensorflow) for our new research-led startup, focussing on the application of artificial intelligence in the real world; particularly smart city simulations and bots. We're looking for a hardcore Machine Learning Scientist/Engineer who thrives wants to work with the latest technology in multi-agent learning algorithms, Gaussian process and reinforcement learning. As a Machine Learning Scientist/Engineer, you will be a core member of the machine learning team; working closely with the Machine Learning researchers, transforming their algorithmic research into highly innovative products which will be attractive and accessible to the world. Key Skills: Machine Learning Engineer/ML Scientist, Tensorflow, C, C, Java, Python, C#, Distributed Algorithms. Distributed systems, BSc, MSc, MPhil, PhD, Post-Doc, Research, R&D, startup, Multithreading.


Building a Better Machine Learning Team

#artificialintelligence

The team who gets the business through the prototype and proof of concept phases, is not the same as the team who will monetize machine learning. This key concept is a starting point for moving forward in the machine learning maturity model. So, what does "better" mean? The business comes into machine learning expecting the technology to grow their bottom line. A better team creates the processes, relationships, and infrastructure to meet the need. Those are a combination of technical and soft skills.


4 Types of Machine Learning Interview Questions for Data Scientists and Machine Learning Engineers

#artificialintelligence

The internet is flooded with top 10, top 20, and even top 200 machine learning interview questions covering a multitude of concepts from bias vs. variance to deep neural networks. While those concepts are important to master in order to ace machine learning interviews, you may feel underprepared and are often caught off-guard during interviews when you are only prepared to solve those problems. The truth is that machine learning interviews are more comprehensive than just a Q&A of basic machine learning concepts. Machine learning interviews evaluate a candidate's capacity to work with a team to solve complex real-world problems using machine learning methodologies. When you google "machine learning interview", it's hard to find articles that give you a full picture of what questions to expect in machine learning interviews.


The Difference Between Data Scientists and ML Engineers

#artificialintelligence

Although they certainly work together amicably and enjoy some overlap concerning expertise and experience, the two roles serve quite different purposes. Essentially, we are differentiating between Scientists who seek to understand the science behind their work, and Engineers who seek to build something that can be accessed by others. Both roles are extremely important, and at some companies, are interchangeable -- for example, Data Scientists at certain organizations may carry out the work of a Machine Learning engineer and vice versa. To make the distinction clear, I'll split the differences into 3 categories; 1) Responsibilities 2) Expertise 3) Salary Expectations. Data Scientists follow the Data Science Process, which may also be referred to as Blitzstein & Pfister workflow.


The Difference Between Data Scientists and ML Engineers - KDnuggets

#artificialintelligence

Although they certainly work together amicably and enjoy some overlap concerning expertise and experience, the two roles serve quite different purposes. Essentially, we are differentiating between Scientists who seek to understand the science behind their work, and Engineers who seek to build something that can be accessed by others. Both roles are extremely important, and at some companies, are interchangeable -- for example, Data Scientists at certain organizations may carry out the work of a Machine Learning engineer and vice versa. To make the distinction clear, I'll split the differences into 3 categories; 1) Responsibilities 2) Expertise 3) Salary Expectations. Data Scientists follow the Data Science Process, which may also be referred to as Blitzstein & Pfister workflow.