Machine Learning Scientist

BigR.io is a technology consulting firm empowering data to drive analytics for revenue growth and operational efficiencies. Our teams deliver software solutions, data science strategies, enterprise infrastructure, and management consulting to the world’s largest companies.

We are looking for a talented ML Scientist to join our dynamic and collaborative startup team to work on the cutting edge of Machine Learning technology. At BigR.io, our high-caliber Scientists in Big Data and Machine Learning are revolutionizing the field of Artificial Intelligence (AI). Our expert Data Science team is led by MIT and Caltech PhDs, specializing in Deep Neural Networks and other Machine Learning approaches (CNN, ANN, NLP, NLU, NLG, etc). As a result, these capabilities make BigR.io a firm the Fortune 1000 can rely on.

* Design, build, and deploy Machine Learning algorithms for advanced analytics. * Perform exploratory data analyses and visualizations on multivariate data sets. * Work closely with our data engineering team to productionize. * Assist in the development of Knowledge Bases and Knowledge Graphs.

**About You** * MS or PhD in Computer Science, Statistics, Physics, or any related Engineering field. * Excellent knowledge of data structures, algorithms and modern OOP techniques. * Experience working with real-world noisy data sets. * Excellent knowledge of one or more of: machine learning, signal processing, statistical modeling, time-series analysis, optimization. * Solid understanding of modern statistical data analysis techniques, such as Bayesian inference, Hidden Markov, and MCMC. **Beneficial Experience** * Good working knowledge of Java / Scala. * Development experience with Python. * Experience with Matlab and/or R. * Experience developing software in a Unix programming environment. * Familiarity with Relational and Non-Relational databases (NoSQL). * Familiarity with cloud computing and big data technologies (Hadoop, Hive, Spark, etc). * Experience with one of the well known neural network libraries, such as TensorFlow, Theano, or Caffe. * Experience with NLP and unstructured data parsing.