Roles and Responsibilities
1. Proven experience coding in Machine Learning/AI techniques including Deep learning techniques (RNN, CNN, GAN, etc), Support Vector Machines; Regularization Techniques; Boosting, Random Forests, Ensemble Methods, image/video/audio processing, Bayesian modeling, time series modeling.
2. Strong implementation experience with high-level languages and frameworks such as R, Python, Perl, Ruby, Scala, Apache Spark, Storm, SAS.
3. Demonstrated ability to work with a variety of Deep learning frameworks including TensorFlow, Keras, Caffe, CNTK, etc
4. Strong hands-on skills in sourcing, cleaning, manipulating and analyzing large volumes of data including SQL and NoSQL databases.
5. Experience with end-to-end modeling projects, from research to solutions to analytic products.
6. Proven experience in using well-established supervised and unsupervised machine learning methods for large industry-strength data analysis problems.