Manager - Data Analytics(Data Insights Lead)

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General Summary:

We are looking for a highly skilled Data Scientist to lead our analytics function within Engineering Operations. This role requires a blend of deep technical expertise, strategic thinking, and people leadership. The individual will own the data ecosystem end‑to‑end—from assessing and organizing all organizational datasets to generating proactive, predictive insights that drive business decisions. 

A key responsibility is to embed a culture where dashboards are not the output—insights are. The expectation is that data must tell a story, influence action, and improve operational efficiency across functions. 

This position also includes managing and mentoring a team of analysts to elevate the organization’s data maturity. 

Minimum Qualifications:

Key Responsibilities 

1. Data Ecosystem Leadership 

• Review all current datasets across Engineering Operations and adjacent functions. 

• Define and implement an optimal data management strategy including architecture, governance, pipelines, retention, and documentation. 

• 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. 

• 10+ years of experience in Data Science / Data Analysis, preferably in data center operations, engineering operations, or technology environments. 

• Knowledge of machine learning, forecasting, anomaly detection, and predictive analytics. 

• Strong proficiency in Python/R, SQL, and advanced analytical/statistical methods. 

• Deep experience with visualization tools such as Power BI or Tableau. 

• Strong ability to analyze highly complex datasets and convert them into strategic insights. 

• Excellent communication and storytelling skills—able to present to both technical and non‑technical audiences. 

• Proven experience working across multiple stakeholders and managing complex analytical projects. 

• High attention to detail, accuracy, and data integrity. 

Preferred 

• Experience with cloud platforms, server architecture, cluster scheduling systems (e.g., LSF). 

• Familiarity with engineering development workflows and compute-heavy environments. 

• Prior experience leading a team 

What Success Looks Like 

• A clean, well‑structured, scalable data ecosystem for Engineering Ops. 

• Predictive insights that help the organization act early rather than react late. 

• Data center utilization is optimized with measurable efficiency gains. 

• Dashboards across the org consistently include insight/commentary. 

• A skilled, motivated analytics team delivering high‑quality output. 

• A cultural shift toward insight‑driven decision-making. 

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