Senior Asset Owner

Responsibilities Define and maintain a roadmap for enhancing the AI and ML ecosystem to support a rapidly expanding set of use cases, capabilities, and environments. Build strategy for Agentic AI & ML and become an influencer in promoting best practices to enterprise AI practitioners. Participate in identifying and assessing opportunities—such as the value of new data sources and analytical techniques—to ensure ongoing competitive advantage. Review work with leadership and partners on an ongoing basis to calibrate deliverables against expectations. Be accountable for ownership of design, development, and maintenance of Model‑as‑a‑Service. Collaborate with partners, Enterprise Data, Data Science, Business, Cloud Enablement Team, and Enterprise Architecture teams. Collaborate with partner teams to create and maintain a Practitioners Guide for AI Platform adoption. Own the delivery of critical milestones for model deployment in the AWS cloud. Adopt AI Platform best practices within the team and promote them to the Data Science and AI practitioners community. Participate in Agile ceremonies, define User Stories, and collaborate with Scrum Master to execute sprints. Provide detailed product status and summary reports to leaders on a regular cadence. Minimum Requirements Must be authorized to work in the U.S. now and in the future. Master’s degree in a related field or at least 5 years of equivalent experience in a research function. Experience leading and managing software development teams. Good understanding of DevSecOps, MLOps, and AgentOps Lifecycle and Agile methodology. Experience in end‑to‑end model development lifecycle, including Gen AI and Agents from ideation through post‑production. Delivery of critical milestones for model deployment in the cloud will be the primary success criteria for this position. Understanding of the Data Science Model Development Process (CRISP‑DM) and the IT Software Development Lifecycle (SDLC) is essential. Familiar with solution design and architecture of data and ML pipelines. Preferred Skills Familiarity with GCP, Vertex AI, AWS, SageMaker, Python Flask. Experience working with Docker, Kubernetes, and EC2 environment. Basic understanding of ML frameworks such as TensorFlow, Anaconda, Scikit‑Learn, H2O. Demonstrable interest in data science or predictive analytics. Ability to work well with a distributed team. Experience with Scrum and Agile framework. Compensation The listed annualized base pay range for this role is: $111,200 - $166,800. Actual base pay could vary based on performance, proficiency, and demonstrated competencies. The base pay is just one component of The Hartford’s total compensation package, which may include short‑term or annual bonuses, long‑term incentives, and on‑the‑spot recognition. Legal and Equal‑Opportunity Statement Equal Opportunity Employer/Sex/Race/Color/Veterans/Disability/Sexual Orientation/Gender Identity or Expression/Religion/Age. #J-18808-Ljbffr The Hartford