Technical Skills
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Strong proficiency in Python, R, SQL.
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Hands-on experience with ML frameworks (TensorFlow, PyTorch, Scikit-learn).
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Expertise in ML architecture, model lifecycle management, and MLOps.
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Strong understanding of statistics, probability, and optimization techniques.
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Experience with Big Data technologies (Spark, Hadoop, Kafka).
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Cloud-native experience (AWS SageMaker, Azure ML, GCP Vertex AI).
Architectural & Design Skills
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Experience designing scalable, secure, and high-performance data science systems.
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Knowledge of data governance, model risk management, and compliance.
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Experience with APIs, microservices, and containerization (Docker, Kubernetes).