● Machine learning libraries, frameworks, techniquesML frameworks such as: Tensorflow,
Keras, PyTorch, Deep Learning etc.
● Deep learning and neural network, supervised and unsupervised machine learning models.
● Leading cloud technologies such as AWS/Azure/GCP.
● ML algorithms including Regression, Classification, Clustering and regularization
techniques such as Dropout, weight decay, generative pre-training.
● Various software development tools, software process, and environments: IDE, debugger,
source control, bug tracking, etc.
● Development of Statistical Models using Machine Learning Python/R.
● Strong experience in building analytical solutions and delivering tangible business value.
● Model development (neural network, deep learning, random forest).
● Building and deploying analytical solutions that have resulted in material financial results and extensive management experience.
● Deep knowledge of math, probability, statistics and algorithms.
● Ability to write robust code in Python, Java and R.