Machine Learning Engineer

Machine Learning Engineer

NL-Amsterdam

8-10 Years

English

Onsite

6 Months

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 Databricks.• 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.QualificationsEducation: 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, PyTorch).

• 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

Certifications: Microsoft Certified: Azure AI Engineer Associate or Azure Data Scientist Associate.