Work closely with IT and Data Engineering to streamline data ingestion, quality, and accessibility.
Champion data standardization and hygiene across teams.
2. Advanced Data Analysis & Predictive Intelligence
Perform deep-dive analysis on high‑volume, complex datasets including data center utilization, engineering job efficiency, infrastructure usage, and operational metrics.
Build predictive models and forecasting frameworks to pre-empt issues and guide proactive planning.
Identify trends, anomalies, risks, and opportunities before stakeholders ask for them.
Develop automated insight pipelines and anomaly detection systems.
3. Insight-Driven Reporting & Storytelling
Establish and enforce standards where every dashboard, report, or metric must include business insights and recommendations.
Present findings to leaders with clarity, business relevance, and strategic interpretation.
Ensure that dashboards for data center metrics and operational KPIs are actionable, not just visual.
4. Data Center Analytics & Efficiency Optimization
Analyze engineering job performance on compute clusters and data center systems.
Evaluate data center efficiency, utilization patterns, and bottlenecks across LSF, servers, and compute workflows.
Provide recommendations to optimize resource planning, scheduling, usage, and cost efficiency.
Develop predictive insights on future usage trends, scaling needs, and operational risks.
5. Cross-Functional Collaboration
Work closely with Engineering, Ops, HR, Facilities, Procurement, Program Management, and Finance.
Serve as the analytics partner for teams, helping them frame problems, interpret data, and take action.
Influence stakeholders to adopt data-driven decision-making practices.
6. Team Leadership
Manage and mentor a small team of analysts and data professionals.
Set direction, review team deliverables, and ensure consistent quality and insight depth.
Build a culture of curiosity, problem‑solving, and ownership within the analytics team.
Qualifications
Required
Bachelor's or Master’s degree in Data Science, Statistics, Computer Science, Engineering, or related field.
