Data Scientist
4 weeks ago
1. Algorithm Development:
Design and implement machine learning algorithms for various applications, including natural language processing (NLP), recommendation systems, and predictive analytics.
Leverage your expertise in deep learning and generative AI to create innovative solutions.
2. Data Processing and Analysis:
Collect, preprocess, and analyze large-scale datasets.
Apply statistical techniques and feature engineering to extract meaningful insights from data.
3. Model Training and Evaluation:
Train and fine-tune ML models using Python and relevant libraries (e.g., TensorFlow, PyTorch).
Evaluate model performance using appropriate metrics and iterate to improve accuracy and efficiency.
4. Deployment and Monitoring:
Deploy ML models to production environments (e.g., cloud platforms such as Azure, AWS, or GCP).
Monitor model performance, troubleshoot issues, and ensure robustness and reliability.
5. Collaboration and Communication:
Collaborate with cross-functional teams, including data scientists, engineers, and product managers.
Communicate results and insights effectively to technical and non-technical stakeholders.
Desired Skills:
• Machine Learning: Strong knowledge of ML algorithms, including supervised and unsupervised learning.
• Generative AI: Experience with generative models (e.g., GANs, VAEs) and their applications.
• Python: Proficiency in Python for data analysis, model development, and scripting.
• Cloud Platforms: Hands-on experience with Azure, AWS, or GCP for model deployment and scalability.
• Natural Language Processing (NLP): Understanding of NLP techniques, sentiment analysis, and
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