Job summary
GeoSpatial Lead Analyst MAP is responsible for advanced geospatial data analysis and ( Growth loop (Preferred) ) modeling to support critical engineering decisions with a primary focus on crack propagation and growth assessment. The role involves hybrid work in a day shift setting applying specialized analytical methods to evaluate structural integrity risks optimize asset performance and drive data backed recommendations that enhance safety
Responsibilities
- Lead complex geospatial analysis projects that integrate multi dimensional spatial datasets with crack propagation and growth models to evaluate structural integrity across large asset networks and critical infrastructure systems.
- Drive the design and refinement of crack propagation analytical frameworks by applying fracture mechanics principles to geospatially distributed assets enabling precise identification of high risk regions and prioritization of mitigation actions.
- Develop advanced geospatial data processing pipelines that clean transform and normalize spatial and temporal datasets ensuring accurate input parameters for crack growth simulations and engineering risk assessments.
- Oversee validation of crack growth predictions by comparing model outputs with field measurements inspection data and historical failure records to continuously improve model accuracy and reliability for operational decision making.
- Provide detailed geospatial risk maps and visualizations that overlay crack growth trajectories with asset locations environmental conditions and usage profiles enabling stakeholders to quickly understand vulnerability hot spots and plan interventions.
- Coordinate with engineering operations and safety teams to translate geospatial and crack propagation insights into practical maintenance schedules inspection plans and asset management strategies that reduce downtime and extend service life.
- Create comprehensive analytical reports and dashboards that summarize geospatial findings crack growth trends and scenario based simulations helping decision makers allocate resources efficiently and justify investments in preventive measures.
- Optimize data workflows for the hybrid work model by leveraging secure remote access tools version controlled repositories and standardized documentation practices to maintain continuity and transparency across onsite and remote collaboration.
- Support continuous improvement of geospatial and crack propagation methodologies by experimenting with new algorithms refining parameter estimation techniques and incorporating emerging industry best practices into the analytical toolkit.
- Collaborate with data engineering teams to define data quality standards for spatial and engineering datasets ensuring reliable ingestion storage and retrieval of information used in crack growth modeling and geospatial risk evaluations.
- Guide adoption of automation and scripting capabilities in geospatial analysis pipelines to reduce manual effort improve repeatability of crack propagation studies and accelerate delivery of insights to cross functional stakeholders.
- Champion the societal impact of robust geospatial and crack growth analytics by demonstrating how proactive asset integrity management enhances public safety protects the environment and supports sustainable infrastructure development.
- Manage day shift analytical activities to ensure timely completion of high priority assessments alignment with project milestones and effective communication of geospatial risk findings to relevant teams within the organization.
Qualifications
- Possess extensive experience in geospatial data analysis including spatial statistics raster and vector processing and map based visualization tailored to support engineering and infrastructure integrity decisions at scale.
- Demonstrate deep hands on expertise in crack propagation and crack growth modeling using fracture mechanics concepts numerical simulation tools and empirical calibration methods applied to real world structural components.
- Apply strong proficiency in programming or scripting for data analysis such as using languages or platforms that enable efficient processing of large geospatial datasets integration with crack growth models and automation of workflows.
- Utilize advanced skills in statistical modeling and uncertainty quantification to evaluate confidence levels in crack propagation predictions and to communicate probabilistic risk metrics to technical and nontechnical stakeholders.
- Bring solid understanding of materials behavior stress analysis and structural health monitoring principles that inform the selection of input parameters and boundary conditions used in crack growth and geospatial risk simulations.
- Leverage experience with enterprise geospatial platforms and data management solutions to design scalable architectures for storing querying and visualizing spatial datasets relevant to asset integrity and crack progression studies.
- Exhibit strong communication and documentation capabilities to present complex geospatial and crack growth findings in clear narrative form including maps charts and written interpretations that support sound operational decisions.
- Maintain familiarity with industry standards and regulatory expectations related to structural integrity assessment crack evaluation techniques and geospatial reporting practices ensuring that analyses align with compliance requirements.
- Show proven ability to work effectively in a hybrid environment coordinating tasks across onsite and remote settings while maintaining secure data handling practices and consistent engagement with project stakeholders.
关于高知特 (Cognizant)
高知特(Cognizant)（纳斯达克代码：CTSH）作为一家AI Builder和相关技术服务提供商，致力于通过打造全栈AI解决方案，帮助企业将人工智能投资转化为实际价值。公司凭借深厚的行业经验、流程优化和工程技术专长，将企业独特的业务场景融入科技系统，赋能组织释放人才潜能，推动切实成果，并帮助全球企业在瞬息万变的环境中保持领先。如需了解更多详情，敬请访问 cognizant.ai 或关注@cognizant。
补充雇佣信息
薪酬信息截至本职位发布之日为准。Cognizant 保留在适用法律允许的范围内随时修改该信息的权利。
申请人可能需要通过现场面试或视频会议的方式参加面试。此外，候选人在每次面试时可能需要出示其当前所在州或政府签发的有效身份证件。
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