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