AI Data Scientist
2 days ago
- Machine Learning Solution Development:
- Design, develop, and implement advanced machine learning models (supervised and unsupervised) to solve complex IT Operations problems, including Event Correlation, Anomaly Detection, Root Cause Analysis, Predictive Analytics, and Auto-Remediation.
- Leverage structured and unstructured datasets, performing extensive feature engineering and data preprocessing to optimize model performance.
- Apply strong statistical modeling, hypothesis testing, and experimental design principles to ensure rigorous model validation and reliable insights.
- AI/ML Product & Platform Development:
- Lead the end-to-end development of Data Science products, from conceptualization and prototyping to deployment and maintenance.
- Develop and deploy AI Agents for automating workflows in IT operations, particularly within Networks and CyberSecurity domains.
- Implement RAG (Retrieval Augmented Generation) based retrieval frameworks for state-of-the-art models to enhance contextual understanding and response generation.
- Adopt AI to detect and redact sensitive data in logs, and implement central data tagging for all logs to improve AI Model performance and governance.
- MLOps & Deployment:
- Drive the operationalization of machine learning models through robust MLOps/LLMOps practices, ensuring scalability, reliability, and maintainability.
- Implement models as a service via APIs, utilizing containerization technologies (Docker, Kubernetes) for efficient deployment and management.
- Design, build, and automate resilient Data Pipelines in cloud environments (GCP/Azure) using AI Agents and relevant cloud services.
- Cloud & DevOps Integration:
- Integrate data science solutions with existing IT infrastructure and AIOps platforms (e.g., IBM Cloud Paks, Moogsoft, BigPanda, Dynatrace).
- Enable and optimize AIOps features within Data Analytics tools, Monitoring tools, or dedicated AIOps platforms.
- Champion DevOps practices, including CI/CD pipelines (Jenkins, GitLab CI, GitHub Actions), infrastructure-as-code (Terraform, Ansible, CloudFormation), and automation to streamline development and deployment workflows.
- Performance & Reliability:
- Monitor and optimize platform performance, ensuring systems are running efficiently and meeting defined Service Level Agreements (SLAs).
- Lead incident management efforts related to data science systems and implement continuous improvements to enhance reliability and resilience.
- Leadership & Collaboration:
- Translate complex business problems into data science solutions, understanding their strategic implications and potential business value.
- Collaborate effectively with cross-functional teams including engineering, product management, and operations to define project scope, requirements, and success metrics.
- Mentor junior data scientists and engineers, fostering a culture of technical excellence, continuous learning, and innovation.
- Clearly articulate complex technical concepts, findings, and recommendations to both technical and non-technical audiences, influencing decision-making and driving actionable outcomes.
- Best Practices:
- Uphold best engineering practices, including rigorous code reviews, comprehensive testing, and thorough documentation.
- Maintain a strong focus on building maintainable, scalable, and secure systems.
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