Data Scientist
6 days ago
Job Summary :
Key Responsibilities :
Model Design and Deployment :
- Design and deploy statistical, Machine Learning (ML), and Deep Learning (DL) models to address complex business issues.- Gain practical experience working with tools and frameworks like Flask, PySpark, PyTorch, TensorFlow, Keras, and Databricks.
- Formulate strategies for data accessibility and augmentation.
- Deploy ML models into production environments (MLOps) on cloud platforms like Azure or AWS.
Data Analysis and Insights :
- Apply predictive and ML techniques such as regression models, XGBoost, random forest, and neuralnetworks.
- Utilize NLP techniques like RNN, LSTM, and Attention-based models, and work with pre-trained models from platforms like Azure and OpenAI.
- Proficiently write efficient SQL queries to pull and manipulate data from databases.
- Develop innovative data visualizations using tools like d3js, dash-plotly, and neo4j to enhance data comprehension.
Technical and Business Collaboration :
- Comprehend business issues and propose valuable, data-driven solutions.- Collaborate with cross-functional teams to integrate AI/ML models into existing systems.
- Use version control tools (GitHub, Bitbucket) for code management and collaboration.
Required Skills :
Core Experience :
- 4 years of experience as a Data Scientist.- Practical knowledge of statistics and operations research methods.
Technical Proficiency :
- Strong hands-on experience in AI/ML, NLP, Deep Learning, and Generative AI/LLMs.- Expertise in frameworks such as TensorFlow and PyTorch.
- Experience with Image Processing using libraries like OpenCV and Pillow.
- Proficiency in deploying ML models into a production environment (MLOps).
Tools & Platforms :
- Primary: AI/ML, TensorFlow, PyTorch, NLP, Image Processing, Gen AI, LLM.- Secondary: Keras, OpenCV, Azure or AWS.
- Experience with various tools like Flask, PySpark, Databricks, and Streamlit.
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