Expert in applied statistics, statistical distributions, statistical tests and a complete understanding of their limitations
Excellent understanding of machine learning techniques and algorithms, such as Regression, k-NN, Naive Bayes, SVM, Random Forests, XGboost, Clustering, Optimization
Strong at forecasting techniques likes ARIMA, SARIMA, Prophet, Croston, UCM etc.
Proficient with Python and R
Design, Architect end-end ML solution, knowledge on productionizing the models
Experience building pipelines and orchestration of workflows in an enterprise environment.
Hands on experience in building NLP and deep learning models.
Good knowledge on NLP techniques like stemming, lemmatization, word2vec, POS tagging, BERT
Strong understanding of the MLOps concepts and tools like mlflow
Experience in developing automation process with MLOps in model management framework like registration, deployment, monitoring and retraining
Good to have knowledge or exposure to Azure Cognitive services, Azure ML, Azure ML Studio, Auto ML, graphML, network graphs
Knowledge in creating data science applications using streamlit, flask etc
Good working knowledge on cloud technologies like Azure and AWS