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:

OR

Associate's degree and 4+ years of Engineering Operations or related work experience.

OR

High School Diploma or equivalent and 6+ years of Engineering Operations or related work experience.

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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