Support the development and maintenance of data driven agentic solutions
Collaborate across teams to learn and apply established best practices and reusable components
Assist in exploring new data sources, research, and models under the direction of senior team members
Execute analytical and modeling tasks as part of a larger project team
Apply standard best practices to develop statistical and machine learning techniques to build models that address business needs and improve data quality and decision making
Review and discuss AI/modeling techniques and results with peers and mentors, clearly communicating findings and incorporating feedback
Contributes to stakeholder communications by preparing summaries, documentation, and analysis to support senior team members in influencing business partners and leaders
Work with guidance to understand business problems and requirements and help identify appropriate modeling approaches
Assist in developing prototypes and frameworks that integrate data and machine learning/predictive modeling into business decision making processes.
Requirements
0 to 2 years of relevant experience
Bachelor's or master's in data science, STEM sciences or related field
One plus year(s) of professional/practical experience as Data Scientist, Machine Learning Engineer, Applied Research professional, or relevant and impactful business internships experience
Prior experience or high interest in Enterprise Risk Management or AI governance related work
Prior experience working in Insurance or related industries
Strong theoretical exposure or practical experience with Infrastructure as Code (IaC) frameworks to provision and manage cloud resources
Strong understanding of CI/CD pipelines, containerization (Docker), observability tools, and cloud security practices
Exposure or practical experiences in Azure Foundry, or AWS Bedrock
Introductory experience or strong interest in AI agent development using at least one framework (e.g., Azure AF, AWS Strands, Google ADK, LangGraph, OpenAI Agents SDK)
Familiarity with building no-code/low-code agents using M365.