Requirements

Practical knowledge and working experience on Statistics and Operation Research methods.
Practical knowledge and working experience in tools and frameworks like Flask, PySpark, Pytorch, tensorflow, keras, Databricks, OpenCV, Pillow/PIL, streamlit, d3js, dashplotly, neo4j.
Good understanding of how to apply predictive and machine learning techniques like regression models, XGBoost, random forest, GBM, Neural Nets, SVM etc.
Proficient with NLP techniques like RNN, LSTM and Attention based models and effectively handle readily available stanford, IBM, Azure, Open AI NLP models.
Good understanding of SQL from a perspective of how to write efficient queries for pulling the data from database.
Hands on experience on any version control tool (github, bitbucket). Experience of deploying ML models into production environment experience (MLOps) in any one of the cloud platforms like Azure and AWS.

Key Responsibilities

Comprehend business issues and propose valuable business solutions.
Design Factual or AI/profound learning models to address business issues.
Design Statistical Models/ML/DL models and deploy them for production.
Formulate what information is accessible from where and how to augment it.
Develop innovative graphs for data comprehension using d3js, dashplotly and neo4j.