Data Scientist, NA

About this role

About Vantage Data Centers

Vantage Data Centers powers, cools, protects and connects the technology of the world’s well-known hyperscalers, cloud providers and large enterprises. Developing and operating across North America, EMEA and Asia Pacific, Vantage has evolved data center design in innovative ways to deliver dramatic gains in reliability, efficiency and sustainability in flexible environments that can scale as quickly as the market demands.

Strategy and Transformation The Strategy and Transformation department is a dynamic and integral component of our business strategy, dedicated to enhancing our market position, business intelligence, and insights through data analysis.

Position Overview This role will be based in Denver, CO. Following our flexible work policy (3 days in-office, 2 days flexible). The Data Scientist plays a critical role in advancing Vantage Data Centers’ analytics, automation, and data-driven decision-making capabilities across North America. This role develops, operationalizes, and scales analytical models that improve forecasting accuracy, optimize data center performance, and enhance operational reliability across Vantage’s rapidly expanding portfolio. The Data Scientist partners closely with Operations, Engineering, Capacity Planning, Finance, Energy & Sustainability, and the Global Data Strategy team to transform raw operational data into actionable insights. This role designs and deploys predictive and prescriptive models that support capacity forecasting, energy optimization, anomaly detection, asset lifecycle management, and customer experience improvements. Operating across regions and collaborating with global stakeholders, the Data Scientist ensures analytical models align with enterprise data architecture, governance standards, and long-term technology strategy. The role contributes to the evolution of Vantage’s data platform, enabling scalable analytics capabilities that support growth, reduce operational friction, and strengthen decision quality across the business.

Essential Job Functions Data Science Strategy & Model Development • Develop predictive and prescriptive models that support operational forecasting, capacity planning, energy optimization, and reliability analysis.

• Identify high-value analytical opportunities across Operations, Engineering, and Customer Experience.

• Build scalable machine learning pipelines that integrate with enterprise data platforms.

• Evaluate model performance and implement continuous improvement mechanisms.

• Result: High-impact analytical models that improve operational efficiency, reliability, and decision quality.

Workflow Integration & Automation • Integrate analytical models into operational workflows, including maintenance planning, incident response, and capacity forecasting.

• Identify opportunities for automation and develop algorithms that streamline manual processes.

• Result: Analytical capabilities embedded directly into operational workflows, improving speed, accuracy, and consistency.

Enterprise Data Alignment & System Integration • Define analytical requirements that inform data engineering, data quality, and data governance priorities.

• Partner with Data Engineering to ensure data pipelines support model accuracy and reliability.

• Collaborate with Enterprise Architecture to align analytical solutions with long-term technology strategy.

• Support reduction of data silos and technical debt through disciplined data integration practices.

• Result: A unified data ecosystem that enables scalable, reliable analytics across the enterprise.

Performance Measurement & Model Governance • Define KPIs and validation frameworks to measure model performance and business impact.

• Ensure models adhere to governance standards, including version control, documentation, and reproducibility.

• Partner with Operations leadership to ensure analytical outputs reflect real-world operational conditions.

• Strengthen the linkage between model performance, operational reliability, and business outcomes.

• Result: Analytical models that are trusted, transparent, and aligned with operational realities.

Additional Duties • Handle additional duties as assigned by management.

Job Requirements • Bachelor’s degree in a quantitative discipline.

• Master’s degree preferred.

• 5–8+ years of experience in data science, machine learning, and software engineering.

• Experience with Full Stack AI assisted development and deployment.

• Experience working with large-scale operational, IoT, or industrial datasets strongly preferred.

• Background in predictive modeling, time-series forecasting, anomaly detection, and optimization algorithms.

• Experience with Azure and Databricks.

• Familiarity with data center operations, energy systems, or mission-critical environments preferred.

• Experience collaborating with cross-functional teams in matrixed organizations.

• Experience deploying models into production environments and integrating with enterprise systems.

• Strong proficiency in Python, SQL, and machine learning frameworks (scikit-learn, TensorFlow, PyTorch).

• Expertise in time-series modeling, statistical analysis, and data visualization.

• Ability to translate complex analytical concepts into clear business language.

• Strong understanding of data engineering principles and model lifecycle management.

• Ability to work across Operations, Engineering, IT, and Data teams.

• Strong communication, structured problem solving, and executive-ready storytelling.

• Ability to balance analytical rigor with operational practicality.

• Travel required is expected to be up to 20%, but may increase over time as business evolves

Physical Demands and Special Requirements The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities t

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