Title:  Ssr. Mechanical Research Engineer – Industrial Equipment Risk Assessment Location: Norwood, MA Job Type: Full-time Overview: We are seeking a research engineer to assess the risks of complex industrial equipment and systems and to develop risk mitigation and loss prevention methodologies. The engineer will be part of a collaborative research team focused on equipment and material science. Key Responsibilities: Review and analyze historical loss data to identify trends and insights related to major industrial equipment such as turbines, generators, transformers, boilers, and pressure vessels.

Collaborate with engineering teams to understand contributing factors and root causes of equipment failures.

Evaluate risk mitigation technologies and develop internal methodologies using physics-based and data-driven approaches.

Support the development and updates of technical standards and best practices.

Design, operate, and troubleshoot testing setups, instrumentation, and software for electromechanical applications.

Conduct condition monitoring experiments and simulate fault conditions to evaluate system effectiveness.

Develop thermal and mechanical models (FEA/CFD) for system and component analysis.

Lead or assist in experimental evaluations and interpret mechanical property data.

Produce technical documentation, reports, and memos.

Propose new research initiatives, including opportunities for collaboration.

Qualifications: Doctoral degree in Mechanical Engineering or related field with at least 5 years of relevant industry experience; or Master’s degree with at least 10 years of experience.

Hands-on expertise in the design, operation, testing, and maintenance of industrial equipment, particularly turbines, generators, pressure vessels, and electromechanical systems.

Strong grounding in engineering fundamentals, including heat transfer, fluid dynamics, vibration and failure mechanics, system modeling, and instrumentation.

Experience with software tools for engineering analysis and programming.

Familiarity with data analytics, statistical modeling, machine learning, and AI techniques for risk assessment and fault diagnostics.