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Data Scientist / Machine Learning Consultants

Job Id : 4692

Jobtitle : Data Scientist / Machine Learning Consultants

Location : New York, NY

Company Name : RTS

Industry : Information Technology

Salary : $80,000 - $150,000  YEAR

Job type : Fulltime

Posted on: 2019-09-17

Required Skills : Data Scientist, Machine Learning, Deep Learning

Benefits : No benefits are available

In depth experience of any of the following highly desirable:

  • Machine Learning Algorithms: Logistic Regression, Linear Regression, Support Vector Machines, Decision Trees, K-Nearest Neighbors, Random Forests, Gradient Boost Decision Trees, Stacking Classifiers, Cascading Models, Nave Bayes, K-Means Clustering, Hierarchical Clustering and Density Based Clustering.
  • Machine Learning Techniques: Principal Component Analysis, Truncated SVD, Data Standardization,L1 and L2 Regularization, Loss Minimization, Hyper Parameter Tuning, Performance Measurement of Models, Featurization and Feature Engineering, Content Based and Collaborative Based Filtering, Matrix Factorization, Model Calibration, productionizing Models, A/B Testing, Point and Interval Estimation, Hypothesis Testing, Cross Validation, Decision Surface Analysis, Retraining Models periodically, t- stochastic neighborhood embedding.
  • NLP algorithms coupled with Deep Learning (ANN and CNN), Time Series Analysis, Speech and Text Analysis (RNN, LSTM), SOMs, Recommender Systems (RBM, AutoEncoders), libraries such as Keras, Tensorflow and PyTorch
  • Deep Learning Techniques such as Back Propagation, Choosing Activation Functions, Weight Initialization based on Optimizer, Avoiding Vanishing Gradient and Exploding Gradient Problems, Using Dropout, Regularization and Batch Normalization, Gradient Monitoring and Clipping Padding and Striding, Max pooling, LSTM, GRU.
  • Deep Learning Artificial Neural Networks, Convolutional Neural Networks, Multi-Layer perceptron s, Recurrent Neural Networks, LSTM, GRU, SoftMax Classifier, Back Propagation, Chain Rule, Choosing Activation Functions, Drop out, Optimization Algorithms, Vanishing and Exploding Gradient, Striding, Padding, Optimized weight Initializations, Gradient Monitoring and Clipping, Batch Normalization, Max Pooling.

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