Data Scientist with Mechanical Engineering

As a Data Scientist specializing in mechanical systems, your role will involve designing, deploying, and maintaining statistical and machine learning models to analyze real-time telemetry data from critical mechanical systems. This includes HVAC systems (AHU/MAU/CRAC/CRAH/RTU), cooling units, chillers, water chemistry, server floor cooling infrastructure, and process cooling (MCU/PCU)/CUBs. Your expertise in mechanical systems will be crucial in understanding equipment behavior, identifying anomalies, and contributing to root cause analysis in mechanical maintenance processes. Key Responsibilities: - Develop equations and logic based on control flow, temperature, pressure, and flux for deployed systems - Apply statistical process control to identify mechanical failures within the systems - Monitor and interpret model outputs to proactively identify early indicators of equipment degradation and potential failures - Collaborate with data center operations and engineering teams to validate anomalies, conduct root cause analysis, and recommend corrective actions - Develop and manage dashboards and reports to visualize key metrics and provide actionable insights to stakeholders - Continuously refine anomaly detection algorithms based on new data and feedback from field operations Qualifications Required: - Bachelor's or Master's Degree in Mechanical Engineering, Systems Engineering, or a related field - Demonstrated experience in Data Science, Computer Science Engineering, or equivalent - Proven experience in data analysis, statistical modeling, and machine learning - Strong proficiency in data analysis tools and programming languages (e.g., Python, R, SQL) - In-depth knowledge of data center mechanical infrastructure, including HVAC systems and cooling units - Exceptional problem-solving abilities and the capacity to work both independently and as part of a team - Excellent communication skills to translate complex data findings into actionable recommendations for non-technical audiences - Strong knowledge of fundamental concepts such as heat transfer, fluid dynamics, and thermodynamics as they apply to cooling systems - Critical experience with telemetry data, various sensor types, and their outputs for effective interpretation in anomaly detection models - Ability to diagnose potential mechanical issues using analytical findings, with experience in root cause analysis being a strong plus You will also be responsible for documenting all findings, analysis methodologies, and resolution steps to contribute to a comprehensive knowledge base for the program.,