Principal Research Scientist - Risk at Neara

Job description

Imagine having the power to stress-test an entire power grid against a hurricane or thunderstorm before the clouds even gather. That is the reality we are creating at Neara. We use advanced machine learning to create engineering-grade, physics enabled digital twins of electricity grids across four continents, this helps asset owners understand their biggest challenges and bring the most viable solutions to life across millions of kilometres of infrastructure. By simulating extreme weather and structural stress at a network-wide scale, we empower the world’s largest utilities to pinpoint risks, optimise investments and build a more resilient global energy future. Our team is a collection of brilliant minds who are fanatical about making a tangible difference in the real world, utilising AI and machine learning to accelerate everything from data classification to complex scenario analysis. We have built a special culture where innovation thrives because everyone owns the mission and we need smart, creative people to help us scale this impact to every corner of the globe. Principal Research Scientist - Risk We are seeking an experienced research scientist to contribute to Neara’s internal research group in topics of system resilience, reliability, and policy. In this role, you will define a research agenda, develop rigorous statistical and decision models, and translate findings into tools and recommendations for utilities managing billions of dollars in capital expenditure. Neara provides network-scale structural, loading, and contextual data across complete overhead electrical systems. As a Principal Research Scientist - Risk, you will use this information to build generalizable models of asset failure and determine the value of additional data, inspections, and interventions. What You Will Do: - Contribute to Neara’s applied research function for infrastructure risk and resilience. - Establish rigorous standards for risk modeling, uncertainty quantification, and decision-making. - Develop predictive models for equipment damage, vegetation failure, outage likelihood and duration, and wildfire-related risk. - Design decision models that quantify the operational and economic consequences of resilience and reliability investments, including asset replacement, vegetation management, and network redesign. - Quantify the value of data including pole loading analysis, visual inspections, and the installation of new devices and remote sensing capabilities, both empirically and by analytical methods. Who You Are: - A PhD in statistics, applied mathematics, physics, computer science, operations research, engineering, or a comparable record of original research and applied technical work. - Proven track record of independently defining, driving, and executing a substantial research agenda as a senior individual contributor or industry principal, demonstrated through influential publications, patents, or production systems. - Recognized subject matter expert with deep technical authority and a history of setting research direction without needing direct management oversight. - Extensive experience in probabilistic modeling, uncertainty quantification, model validation, scientific computing capabilities and decision-making under incomplete information. - Substantial experience modeling operational systems. Particularly relevant topics include insurance or catastrophe modeling; weather prediction and climate hazard; outages in power systems; and reliability engineering. What We Offer: - A highly competitive compensation package including an equity component to ensure you directly benefit from Neara’s sustained success and impact on critical infrastructure. - The opportunity to work on a cutting-edge, 3D physics-enabled platform used to anticipate the impact of extreme events, reduce infrastructure failure, and direct investment. Access to unique network-scale utility data unavailable in other industrial research settings. - The opportunity to help develop and build Neara’s research modeling function, establishing the agenda, identifying preferred methods and working on novel under-specified problems. - A central role in shaping Neara’s long-term product roadmap, analytical standards and customer advisory services. - Flexible hours and working arrangements designed to help you do your best work from where you work best. #LI-NT1 #LI-DNP

Requirements

Must-haves

Imagine having the power to stress-test an entire power grid against a hurricane or thunderstorm before the clouds even gather. That is the reality we are creating at Neara.
By simulating extreme weather and structural stress at a network-wide scale, we empower the world’s largest utilities to pinpoint risks, optimise investments and build a more resilient global energy future.
Principal Research Scientist - Risk
Contribute to Neara’s applied research function for infrastructure risk and resilience.
Establish rigorous standards for risk modeling, uncertainty quantification, and decision-making.
Develop predictive models for equipment damage, vegetation failure, outage likelihood and duration, and wildfire-related risk.
Design decision models that quantify the operational and economic consequences of resilience and reliability investments, including asset replacement, vegetation management, and network redesign.
Quantify the value of data including pole loading analysis, visual inspections, and the installation of new devices and remote sensing capabilities, both empirically and by analytical methods.
A PhD in statistics, applied mathematics, physics, computer science, operations research, engineering, or a comparable record of original research and applied technical work.
Recognized subject matter expert with deep technical authority and a history of setting research direction without needing direct management oversight.
Extensive experience in probabilistic modeling, uncertainty quantification, model validation, scientific computing capabilities and decision-making under incomplete information.
Substantial experience modeling operational systems. Particularly relevant topics include insurance or catastrophe modeling; weather prediction and climate hazard; outages in power systems; and reliability engineering.

Nice-to-haves

None flagged.

Application prep

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Keywords to mirror

machine learningai

Platform: ashby
Effort: quick