
Data Scientist
2 weeks ago
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
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