Data Scientist - Analytics & Reporting

Overview:

Clarium is seeking a highly skilled and analytical Performance Engineer with strong expertise in predictive modeling, data analysis, and reporting. The ideal candidate will play a critical role in analyzing performance data and supporting the scalability and efficiency of our OpenShift container platform (OCP) and legacy application environments. If you’re passionate about data-driven insights and performance engineering, we invite you to apply for this exciting opportunity.

Key Responsibilities:

Maintain and enhance predictive models for performance engineering and capacity forecasting across OpenShift and legacy systems.

Analyze production usage data to identify trends, anomalies, and capacity constraints.

Collect and consolidate performance data from various sources (monitoring tools, logs, telemetry systems, etc.).

Compare real-world usage with test environment simulations to refine forecasting models.

Develop and maintain dashboards and visual reports on capacity headroom and performance metrics.

Monitor infrastructure performance (CPU, memory, disk, etc.) and proactively identify optimization opportunities.

Collaborate with engineering, DevOps, and business stakeholders to align capacity plans with future demand.

Provide data-driven insights and recommendations to ensure optimal system performance and scalability.

Document capacity planning strategies, models, and best practices.

Must-Have Skills:

7+ years of experience in data analysis, predictive modeling, or performance engineering.

Strong hands-on experience with OpenShift container platform infrastructure.

Solid background in data analysis and performance reporting.

Excellent analytical thinking and problem-solving skills.

Strong communication and collaboration skills.

High attention to detail and accuracy.

Preferred Qualifications:

Experience with containerization platforms (e.g., Docker, Kubernetes).

Familiarity with cloud platforms (AWS, Azure, or Google Cloud).

Experience with monitoring/logging tools (e.g., Prometheus, Grafana, ELK).

Proficiency in data visualization tools (e.g., Tableau, Power BI).

Working knowledge of scripting languages (e.g., Python, Bash).

Exposure to performance testing tools (e.g., JMeter, LoadRunner, Gatling).

Understanding of DevOps methodologies and ITIL practices.

Experience with agile project management tools and methodologies.

Familiarity with business intelligence or data warehousing concepts.