Data Scientist

Required Skills:

•   Python Programming: Strong proficiency in Python.
•   Statistics and Operations Research: Practical knowledge and working experience.
•   AI/ML Tools and Frameworks: Experience with Flask, PySpark, PyTorch, TensorFlow, Keras, Databricks, OpenCV, Pillow/PIL, Streamlit, d3js, DashPlotly, and Neo4j.
•   AWS Services: Hands-on experience with AWS AI/ML services like Sagemaker, Canvas, and Bedrock.
•   Machine Learning Techniques: Understanding of predictive and ML techniques like regression models, XGBoost, random forest, GBM, neural networks, SVM, etc.
•   NLP Techniques: Proficient with RNN, LSTM, and attention-based models; experience with models from Stanford, IBM, Azure, OpenAI.
•   SQL: Good understanding of SQL for efficient data querying.
•   Version Control: Hands-on experience with version control tools like GitHub or Bitbucket.
•   MLOps: Experience deploying ML models into production on platforms like Azure and AWS.
•   Business Analysis: Ability to understand business needs and map them to business processes.
•   Agile Methodology: Experience with agile project delivery.
•   Visualization: Good at conceptualizing and visualizing end-to-end business needs both at a high level and in detail.
•   Communication: Good communication, listening, and probing skills.
•   Analytical Skills: Strong analytical and problem-solving skills.
•   Interpersonal Skills: Strong interpersonal skills, collaboration with team members, and ability to work effectively in a team.

 Key Responsibilities:

•  Understand and address business issues with valuable solutions.
•  Design factual, AI, and deep learning models to solve business problems.
•  Develop and deploy statistical, ML, and DL models into production.
•  Identify and augment accessible information sources.
•  Create innovative data visualization graphs using tools like d3js, DashPlotly, and Neo4j.

 Preferred Certifications:

•  AWS Specialty Certification in Data Analytics
•  Machine Learning