Data Scientist

A strong level of proficiency in python programming.
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.
Hands on experience in Analytics/AI-ML AWS services like Sagemaker, Canvas, Bedrock.
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.
Understanding business needs / mapping it to the business processes.
Hands on experience in agile project delivery.
Good in conceptualizing and visualizing end to end business needs both at high level as
well as detailed.
Good in articulating the business needs.
Good analytical and problem-solving skills.
Good communication, listening and probing skills.
Strong inter-personnel skills.
Should collaborate with other team members and work as team.

Job Description:
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.
Preferred Certification: (Good to have)
AWS Specialty Certification in Data Analytics, Machine Learning.