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People management: Individual contributor stated — people management source
Design, build, and evaluate generative AI solutions and agentic systems Develop and maintain data semantic layers and knowledge graphs for enterprise-scale data accessibility Build evaluation frameworks and tools to assess GenAI systems' performance, reliability, and safety Collaborate with other data scientists, machine learning engineers, data engineers, and business partners Champion best practices and tooling across a broad data science community Lead cross-functional working groups and contribute to innovation in AI/ML methods Communicate complex technical findings clearly to non-technical stakeholders Serve as a technical consultant on complex, high-impact projects Please note this is an individual contributor role Broad knowledge of predictive analytic techniques and statistical diagnostics of models. Advanced knowledge of predictive toolset; reflects as expert resource for tool development. Demonstrated ability to exchange ideas and convey complex information clearly and concisely. Ability to establish and build relationships within and outside the organization. Ability to give effective training and presentations to management and other groups. Ability to use results of analysis to persuade team, department management or senior management to a particular course of action. Broad knowledge of business drivers and market context. Has a value driven perspective with regard to understanding of work context and impact. Competencies typically acquired through a Ph.D. degree (in Statistics, Mathematics, Economics, Actuarial Science or other scientific field of study) and a minimum of 3 years of relevant experience, a Master`s degree (scientific field of study) and a minimum of 6 years of relevant experience or may be acquired through a Bachelor`s degree (scientific field of study) and a minimum of 8 years of relevant experience. Strong foundation in data science and machine learning Proven MLOps expertise across the complete data science lifecycle Experience building GenAI solutions and agentic systems Familiarity with GenAI evaluation methodologies Experience with data semantic layers and/or knowledge graphs Track record of cross-functional collaboration in a large enterprise environment Ability to work ET hours
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Design, build, and evaluate generative AI solutions and agentic systems Develop and maintain data semantic layers and knowledge graphs for enterprise-scale data accessibility Build evaluation frameworks and tools to assess GenAI systems' performance, reliability, and safety Collaborate with other data scientists, machine learning engineers, data engineers, and business partners Champion best practices and tooling across a broad data science community Lead cross-functional working groups and contribute to innovation in AI/ML methods Communicate complex technical findings clearly to non-technical stakeholders Serve as a technical consultant on complex, high-impact projects Please note this is an individual contributor role Broad knowledge of predictive analytic techniques and statistical diagnostics of models. Advanced knowledge of predictive toolset; reflects as expert resource for tool development. Demonstrated ability to exchange ideas and convey complex information clearly and concisely. Ability to establish and build relationships within and outside the organization. Ability to give effective training and presentations to management and other groups. Ability to use results of analysis to persuade team, department management or senior management to a particular course of action. Broad knowledge of business drivers and market context. Has a value driven perspective with regard to understanding of work context and impact. Competencies typically acquired through a Ph.D. degree (in Statistics, Mathematics, Economics, Actuarial Science or other scientific field of study) and a minimum of 3 years of relevant experience, a Master`s degree (scientific field of study) and a minimum of 6 years of relevant experience or may be acquired through a Bachelor`s degree (scientific field of study) and a minimum of 8 years of relevant experience. Strong foundation in data science and machine learning Proven MLOps expertise across the complete data science lifecycle Experience building GenAI solutions and agentic systems Familiarity with GenAI evaluation methodologies Experience with data semantic layers and/or knowledge graphs Track record of cross-functional collaboration in a large enterprise environment Ability to work ET hours