Lead Machine Learning Modeler
1 day ago
We are seeking a highly skilled Machine Learning Engineer to join our team. The successful candidate will be responsible for developing, training, and validating predictive models using machine learning techniques to drive business growth.
The ideal candidate will have strong proficiency in Python (Pandas, NumPy, Scikit-learn) and deep learning frameworks (PyTorch/TensorFlow). Experience with ML Ops tools/frameworks (MLflow, Kubeflow, Airflow, SageMaker, Vertex AI, or Azure ML) for streamlined operations is also essential.
The Machine Learning Engineer will collaborate with data engineers and business teams to define data requirements and success metrics for informed decision-making. They will deploy machine learning models into production using best practices for scalability and reliability.
In addition, the successful candidate will build automated pipelines for model training, testing, and retraining to ensure optimal performance. They will monitor model drift and performance degradation, making continuous improvements to maintain high standards.
The role requires a strong background in statistics, machine learning algorithms, and model evaluation techniques for accurate insights. Familiarity with version control (Git) and CI/CD pipelines for collaborative development is also necessary.
Key Responsibilities:
- Develop, train, and validate predictive models using machine learning techniques.
- Collaborate with data engineers and business teams to define data requirements and success metrics.
- Deploy machine learning models into production using best practices for scalability and reliability.
- Build automated pipelines for model training, testing, and retraining.
- Monitor model drift and performance degradation, making continuous improvements.
Required Skills and Qualifications:
- Strong proficiency in Python and deep learning frameworks.
- Experience with ML Ops tools/frameworks for streamlined operations.
- Good understanding of data engineering concepts for seamless data integration.
- Hands-on experience with containerization and orchestration for efficient deployment.
- Knowledge of cloud platforms for scalable model deployment.
- Strong background in statistics, machine learning algorithms, and model evaluation techniques.
- Familiarity with version control and CI/CD pipelines for collaborative development.
Benefits:
- Paid training and certification opportunities.
- Opportunities for career growth and professional development.
- A dynamic and collaborative work environment.
Others:
- Bachelor's/Master's degree in Computer Science, Data Science, Statistics, or related field.
- Experience in handling large-scale data pipelines and real-time model serving.
- Exposure to feature stores and automated model monitoring.
- Strong problem-solving, analytical, and communication skills.
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