Your mission We are searching for great machine learning engineers to join the team responsible for: · Extending Criteo's large scale distributed machine learning library (e.g., implementing new distributed and scalable machine learning algorithms, improving their performance) · Building and improving prediction models for ad targeting; proving the business value of the changes and deploying them to production · Gathering and analyzing data, performing statistical modeling You'll have the opportunity to work on highly challenging problems with both engineering and scientific aspects; for example: · Click prediction:ÂHow do you accurately predict in less than a millisecond if the user will click on an ad? Thankfully, you have billions of datapoints to help you.Â · Offline testing:ÂYou can always compute the classification error on a model predicting the click probability. But will it really correlate with the online performance of this model?ÂÂ · Explore / exploit:ÂIt's easy, UCB and Thomson sampling have low regret. But what happens when new products come and go and when each ad displayed changes the reward of each arm? But what do you do when all data are not equal and when you must distribute the learning overÂthousandsÂof nodes? To qualify for this mission, you need: · MS degree in Computer Science or related quantitative field with 3 years of relevant experience or Ph.D degree in Computer Science or related quantitative field · Good understanding of the mathematical foundations behind machine learning algorithmsÂ · Great coding skills.
Hello everyone, I have a question: according to the tip number 2, what are exactly the desirable skills found in nuclear physicists, mechanical engineers or bioinformatics experts? As a Computer Scientist I want to become a solid Data Scientist and Data Engineer, whereby I want to increase and polish my skills and learn from the others as well. If any expert could answer my question, I will thank you deeply.
Michael Brzustowicz is a physicist turned data scientist. After a PhD from Indiana University, Michael spent his post doctoral years at Stanford University where he shot high powered Xrays at tiny molecules. Jumping ship from academia, he worked at many startups (including his own) and has been pioneering big data techniques all the way. Michael specializes in building distributed data systems and extracting knowledge from massive data. He spends most of his time writing customized, multithreaded code for statistical modeling and machine learning approaches to everyday big data problems.
We all know that R and Python are both used for data science. Machine learning can be done with both. They are probably the two beginner's programming languages for your foray into data science. There are plenty of software engineers who are either transitioning into data science by becoming data scientists, data engineers, and machine learning engineers, or they are working on AI software projects. If you are a programmer or a software engineer on this path, then this article is for you.
For all the ambitious candidates, learn how to become an Artificial Intelligence Engineer. Artificial Intelligence is budding in the last period at a speedy pace. Artificial intelligence is taking place in all the astonishing high-impact assignments in many sectors. For all the ambitious applicants, this is the precise period to become an AI engineer. There are several artificial intelligence specialists coming forward and distributing their missions.