
Leading Machine Learning Solutions
3 days ago
Developing, implementing, and optimizing machine learning models and systems is a key responsibility of this role. Collaboration with data scientists, software engineers, and other collaborators is essential to translate data insights into practical, scalable solutions.
- Design, build, and deploy machine learning models and algorithms to solve specific business problems. Optimize models for performance and scalability.
- Work with large datasets to preprocess, clean, and transform data for use in machine learning models. Develop and maintain data pipelines.
- Monitor and evaluate the performance of deployed models, making adjustments and improvements as needed to ensure accuracy and reliability.
- Collaborate with multi-functional teams such as data scientists, analysts, and product managers to understand requirements and deliver machine learning solutions that meet business needs.
- Stay up-to-date with the latest research and trends in machine learning and artificial intelligence. Explore and implement new techniques and technologies to enhance model performance.
- Document model development processes, code, and standard methodologies. Provide clear and comprehensive reports on model performance and metrics.
Requirements:
- Bachelor's degree in computer science, data science, statistics, or a related field, or a master's degree or higher is preferred.
- More than three years of experience in machine learning engineering or a related role, with a strong track record of developing and deploying machine learning models.
- Proficiency in programming languages such as Python or R.
- Experience with machine learning frameworks (e.g., Go, TensorFlow, PyTorch, scikit-learn).
- Experience with Azure Cognitive services.
- Experience with data processing and manipulation tools and libraries (e.g., pandas, NumPy).
- Strong analytical and problem-solving skills, with the ability to handle complex and large-scale datasets.
- Experience with deploying machine learning models to production environments, including knowledge of containerization technologies (e.g., Docker or equivalent) and cloud platforms, Microsoft Azure is preferred.
- Excellent verbal and written communication skills, with the ability to explain technical concepts to non-technical collaborators.
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