Ability to design and implement workflows of Linear and Logistic Regression, Ensemble Models (Random Forest, Boosting) using R/Python Demonstrable competency in Probability and Statistics, ability to use ideas of Data Distributions, Hypothesis Testing and other Statistical Tests. Must have experience in dealing with outliers, denoising data and handling the impact of pandemic like situations. Should be able to perform EDA of raw data feature engineering wherever applicable Demonstrable competency in Data Visualisation using the Python/R Data Science Stack. Should be able to leverage cloud platforms for training and deploying large scale solutions. Should be able to train and evaluate ML model using various machine learning and deep learning algorithm. Retrain and maintain model accuracy in deployment. Knowledge of cloud platforms (AWS, Azure and GCP) Exposure to No SQL databases (MongoDB, Cassandra, Cosmos DB, HBase) Forecasting experience in products like SAP, Oracle, Power BI, Qlik, etc. Proficiency in Excel (Power Pivot, Power Query, Macros, Charts) An experience with large data sets and distributed computing (Hive/Hadoop/Spark) Transfer learning using state of art models in different spaces - vision, NLP and speech. - Integration with external services and Cloud API. Working with data annotation approaches and tools for text, images and videos Should be able to package deploy large scale models on on-premise systems using multiple approaches including docker. Should be able to take complete ownership of the assigned project Experience of working in Agile environments Well versed with JIRA or equivalent project tracking tool

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