
Data Science Strategist
9 hours ago
Key Responsibilities:
- Develop, train, and validate predictive and analytical models using machine learning techniques to drive business growth.
- Collaborate with cross-functional teams to define data requirements and success metrics that align with company objectives.
- Deploy machine learning models into production using industry best practices and ensure seamless integration with existing systems.
- Design and implement automated pipelines for model training, testing, monitoring, and retraining to optimize performance and scalability.
- Monitor model drift, performance degradation, and continuously improve the accuracy of predictive models.
- Implement DevOps practices for machine learning workflows to ensure efficient deployment and continuous improvement.
Technical Skills Required:
- Proficient in Python programming language and familiar with popular libraries such as Pandas, NumPy, Scikit-learn, and PyTorch/TensorFlow.
- Experience with ML Ops tools and frameworks such as MLflow, Kubeflow, Airflow, SageMaker, Vertex AI, or Azure ML.
- Strong understanding of data engineering concepts including ETL, data pipelines, and APIs.
- Familiarity with containerization and orchestration using Docker and Kubernetes.
- Knowledge of cloud platforms such as AWS, GCP, or Azure for scalable machine learning model deployment.
- Background in statistics, machine learning algorithms, and model evaluation techniques.
- Familiarity with version control using Git and CI/CD pipelines.
Preferred Qualifications:
- 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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