Data Scientist, Reliability Engineering

The Data Scientist, Reliability Engineering is responsible for developing and deploying analytics, models, and scalable data products that improve reliability outcomes across mission critical power, cooling, and controls systems. This role partners with Reliability Engineering and Operations Engineering to transform operational datasets (e.g., CMMS, BMS/EPMS, condition monitoring, incident records) into actionable insights that enable proactive risk reduction, improved maintenance strategy effectiveness, and stronger reliability governance.
This position is an individual contributor role focused on applied data science and engineering grade analytics. The Data Scientist does not serve as the final engineering authority for risk acceptance; instead, the role produces trusted data products and decision support that strengthen reliability strategy, incident learning, and lifecycle planning across the portfolio.

Key Responsibilities

Reliability Analytics & Modeling

·       Build statistical and machine‑learning models that detect degradation, anomalies, and failure precursors in mission‑critical infrastructure data.

·       Apply reliability‑relevant methods (e.g., time series analysis, survival/MTTF modeling, probabilistic risk signals, forecasting) to quantify failure likelihood and uncertainty.

·       Develop repeatable analytical frameworks to support Reliability Engineering workflows (e.g., maintenance strategy effectiveness, spares exposure, recurring failure patterns).

Data Integration & Quality

·       Design and maintain data pipelines that integrate CMMS/work management, BMS/EPMS telemetry, condition-monitoring outputs, and incident/RCA artifacts.

·       Establish data quality standards, validation checks, and documentation to improve trust, repeatability, and scalability of reliability analytics.

·       Partner with platform/IT stakeholders to ensure analytics solutions are secure, supportable, and fit-for-scale.

Operational Decision Support

·       Translate analytical findings into clear insights and recommendations that Reliability Engineers and leaders can act on (risk reduction, maintenance optimization, capital planning support).

·       Build and maintain dashboards, scorecards, and leading indicators that enable portfolio-level visibility into degradation trends and reliability risk.

·       Support post-incident learning by structuring data for RCA/FMEA and identifying latent/systemic patterns across events.

Productization & Enablement

·       Deliver analytics as reusable “products” (models, pipelines, dashboards, playbooks) with defined inputs/outputs, monitoring, and change control.

·       Educate stakeholders on interpretation, limitations, and uncertainty to enable responsible use of predictive/diagnostic outputs.

 

Qualifications

·       Bachelor’s degree in Data Science, Statistics, Applied Math, Engineering, Computer Science, or similar quantitative field.

·       4+ years (mid-level) or 6+ years (senior-level) of applied analytics/data science experience in operational, industrial, infrastructure, or high-availability environments.

·       Proficiency in Python for data analysis/modeling and SQL for data extraction/joins across relational datasets.

·       Demonstrated ability to work with noisy, imperfect operational datasets and produce reliable, explainable insights.

·       Strong communication skills: able to explain models and results to engineering and operations audiences.

Preferred Experience

·       Experience with CMMS/work management data, BMS/EPMS telemetry, historian/time-series data, or condition monitoring programs.

·       Experience applying analytics to reliability, mai