Machine Learning Engineer
Key Responsibilities
Advanced Analytics & Machine Learning
Design and implement scalable ML models to support business goals in areas such as risk management, customer intelligence, and operational efficiency.Data-Driven Innovation
Collaborate with cross-functional teams to identify and prioritize opportunities for AI/ML applications that deliver measurable business value.Model Governance & Compliance
Ensure all models adhere to regulatory standards and internal governance frameworks, including explainability, fairness, BCBS 239, and GDPR compliance.Cloud & Modern Data Stack
Utilize cloud platforms (e.g., AWS, Azure, GCP) and modern data tools to build robust, production-ready data science solutions.Stakeholder Engagement
Partner with business, technology, and compliance teams to translate complex data insights into actionable strategies and communicate findings effectively.
Required Qualifications
- Bachelor's or Master's degree in Computer Science, Data Science, Statistics, or a related field.
- Proven experience in building and deploying machine learning models in production environments.
- Proficiency in Python, SQL, and ML libraries such as scikit-learn, TensorFlow, or PyTorch.
- Experience with cloud platforms (AWS, Azure, or GCP) and containerization tools (Docker, Kubernetes).
- Strong understanding of data governance, model risk management, and regulatory compliance.
Preferred Qualifications
- Experience in financial services, fintech, or regulated industries.
- Familiarity with MLOps practices and tools (e.g., MLflow, Kubeflow, Airflow).
- Knowledge of data privacy laws and ethical AI principles.
FAQs
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