Machine Learning
1 maand geleden
Description:
1.Model Development & Optimization
• Design and develop machine learning models tailored to banking needs, including credit scoring, fraud detection, customer segmentation, and churn prediction.
• Optimize model performance for real-time predictions and recommendations.
• Use advanced techniques like deep learning, NLP, and time-series analysis as required.
2. Data Preparation & Feature Engineering
• Work with large-scale banking datasets to perform ETL processes using Azure Data Factory and Azure Data bricks.
• Conduct feature engineering, data cleaning, and transformation to prepare datasets for machine learning.
3. Deployment & Monitoring
• Deploy ML models on Azure Machine Learning Service and set up monitoring for real-time and batch processing.
• Manage versioning, scalability, and maintenance of models using Azure DevOps.
4. Collaboration & Stakeholder Engagement
• Collaborate with data scientists, software engineers, and stakeholders to understand requirements and translate them into ML solutions.
• Engage with banking teams to align ML models with compliance and regulatory standards.
5. Documentation & Reporting
• Document model design, assumptions, and results for transparency and reproducibility.
• Generate reports and present insights to technical and non-technical stakeholders.
Requirements
Education:
Bachelor’s or Master’s in Computer Science, Data Science, Machine Learning, or related fields.
Skills:
• Strong programming skills in Python and familiarity with ML libraries (e.g., Scikit-Learn, TensorFlow, PY-Torch).
• Proficiency in SQL and database systems, with knowledge of Azure SQL Database or Azure Cosmos DB.
• Experience with MLOps, containerization (e.g., Docker), and deployment in Azure.
• Strong understanding of banking use cases like credit scoring, fraud detection, and risk management.
Certifications:
Microsoft Certified: Azure AI Engineer Associate or Azure Data Scientist Associate.
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