Top Senior Data Science Developers of July 2017
CTO/Deep learning Researcher (NLP) @ Neuron
I am the CTO/Deep Learning Researcher(NLP) at Neuron and supervise the research and development work of Neuron's core NLP platform. Area of work: - Deep Learning in NLP - Recurrent/Recursive Neural Nets, Convolutional Neural Nets, Attention Networks - Sequence Labelling, Sentence Classification, Auto encoders, Encoder-Decoder Models, Sentence Embeddings, Predictive Statistical Modelling - Machine Learning - Regression, Ensemble Learning, Neural Nets, Recommendation Systems Interested in working with: - Memory Networks - Deep Reinforcement Learning - Deep Generative Models - Optimization methods for DNNs - Deep Learning for Computer Vision
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*** 100+ sessions COMPLETED with ONLY FIVE STARS ratings! *** I help companies use data to predict important tasks using machine learning models. Answering questions like: Will a customer stop buying from us? How much of this product are we selling next month? What product this customer is likely to be interested in buying? And many more! Check some articles I write about ML at: http://www.mariofilho.com I am a Kaggle Competitions Grandmaster, and my highest rank is 12th of 46,000+ data scientists. Machine Learning Competitions Results - 1st of 1323 at Caterpillar Tube Pricing Goal: model quoted prices for industrial tube assemblies that Caterpillar buys from multiple suppliers to help in the pricing of its final product - 1st of 974 at Telstra Network Disruptions Goal: create a model to predict service faults on Australia's largest telecommunications network - 3rd of 548 at Avito Duplicate Ads Detection Goal: create a model to detect duplicate ads in a Russian classifieds platform - 6th of 985 at Facebook Recruiting IV: Human or Robot? (Top 0.6%) Goal: Predict if an online bid is made by a machine or a human - 13th of 2125 at Home Depot Product Search Relevance (Top 0.6%) Goal: Predict the relevance of search results on homedepot.com
CTO at Draft AI | #1 CodeMentor for Python, JS, Node, React for now - still working on the others ;)
I'm a Scotsman living in London. I have over 10 years experience coding. I have a first in Computer Science, worked at one of the top cyber security consultancies and am doing some freelance work alongside my startup.
I like solving problems with code and data.
My personal website is https://otrenav.com I'm a co-founder at Datata http://datata.mx Research assistant at ITAM https://itam.mx
Fullstack Development Consultant and Entrepreneur
Solve your problems
3 years machine learning experience. 14 years software development experience. Deep Learning Foundation Nanodegree (Udacity) graduate Familiar with machine learning and deep learning techniques, libraries and toolkits. Familiar with scikit-learn, tensorflow, keras, nltk https://www.kaggle.com/maggiezhou I have participated in some Kaggle competitions: Home Depot Product Search Relevance, placed 59th/2125 (top 3%) Allstate Purchase Prediction Challenge, placed 94th/1568 (top 6%) Homesite Quote Conversion, placed 158th/1764 (top 9%) Acquire Valued Shoppers Challenge, placed 89th/952 (top 10%) Rossman Store Sales, placed 480th/3303 (top 15%) Otto Group Product Classification Challenge, placed 532th/3514 (top 16%)
Let me help solve your toughest problems.
I pride myself on my ability to write solid, well documented and fully tested software. With the projects that I have worked on, my software has not only needed to hold up to use by its users, it has also needed to be able to withstand scrutiny in court. As a result, I take writing quality code very seriously. Over the last few years I have worked on creating a platform to revolutionize the way that investigators (of all kinds) explore unstructured data, primarily text documents. I did this by taking advantage of the latest technologies and techniques in information retrieval and natural language processing. I also created a simple yet powerful language that allows investigators to ask deeper questions of the data. The end result of all of that effort is a system that allowed a forensic accounting team, of just two people, to go through a collection of over 500,000 documents and find emails that allowed them to receive $4,000,000 in damages for their client. The original target was only $250,000.
ex-Intern at The New York Times and Google. Machine learning and functional programming nut.
Full stack Ruby on Rails Developer and AWS Cloud Architect
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