Data Scientist MGR

You should have the ability to design and implement workflows of Linear and Logistic Regression, Ensemble Models (Random Forest, Boosting) using R/Python. Demonstrating competency in Probability and Statistics, you should be capable of utilizing ideas of Data Distributions, Hypothesis Testing, and other Statistical Tests. Your experience should include dealing with outliers, denoising data, and handling the impact of pandemic like situations. Performing Exploratory Data Analysis (EDA) of raw data and feature engineering wherever applicable is crucial. You should possess demonstrable competency in Data Visualization using the Python/R Data Science Stack. Leveraging cloud platforms for training and deploying large-scale solutions is essential. Being able to train and evaluate ML models using various machine learning and deep learning algorithms is a must. You should also be skilled in retraining and maintaining model accuracy in deployment. Knowledge of cloud platforms such as AWS, Azure, and GCP is required. Exposure to NoSQL databases (MongoDB, Cassandra, Cosmos DB, HBase) is preferred. An understanding of forecasting experience in products like SAP, Oracle, Power BI, and Qlik is beneficial. Proficiency in Excel (Power Pivot, Power Query, Macros, Charts) is expected. Having experience with large datasets and distributed computing (Hive/Hadoop/Spark) is advantageous. Knowledge of transfer learning using state-of-the-art models in different spaces like vision, NLP, and speech is a plus. Integration with external services and Cloud API is also a part of the role. Working with data annotation approaches and tools for text, images, and videos is desirable. You should be able to package and deploy large-scale models on on-premise systems using multiple approaches, including Docker. Taking complete ownership of the assigned project is necessary. Experience of working in Agile environments and being well-versed with JIRA or equivalent project tracking tools is expected.,