Develop scalable tools leveraging machine learning and deep learning models to solve real-world problems in areas such as Speech Recognition, Natural Language Processing and Time Series predictions
Apply sophisticated machine learning methods to banking applications including risk assessment, trading models, customer relationship management, and pricing models
Create an effective roadmap towards the deployment of a production-level machine learning application.
Skills & Qualifications:
MS or PhD in a Quantitative Discipline, e.g. Computer Science, Mathematics, Operations Research, Data Science
Experience in Deep Learning: DNN, CNN, RNN/LSTM, GAN or other auto encoder (AE).
Strong knowledge and ability to code in Python, Java or C++
Extensive experience with machine learning APIs and computational packages (TensorFlow, Theano, PyTorch, Keras, Scikit-Learn, NumPy, SciPy, Pandas, statsmodels).
Familiarity with basic data table operations (SQL, Hive, etc.)
Experience with big-data technologies such as Hadoop, Spark, SparkML