App & AI Business System Analyst

Job Description

Responsibility

• Leverage subject matter expertise to improve processes and methodologies, architect solutions and independently analyze multi-step challenges, actively identifying opportunities to increase value or reduce risk, and influencing decisions across teams and functions
• Use subject matter expertise to improve methodologies, policies, and customer solutions
• Connect teamwork and deliverables to broader business outcomes, identifying opportunities to increase value or reduce risk, especially by leveraging new efficiencies enabled by technology like AI where possible
• Advise peers and stakeholders by sharing insights, offering constructive feedback, and supporting sound decision-making
• Navigate cross-functional dependencies by understanding colleagues perspectives and priorities, fostering alignment and effective collaboration across teams
• Lead cross-functional projects to drive team-level objectives, operating as a Trusted Advisor to solve complex problems and set or influence team goals
• Lead and deliver end to end ongoing work, projects or programs that balance short term results with longer term impact
• Operate with a high degree of independence, evaluating, adopting, and scaling tools and practices (including AI) to improve organizational efficiency and embed continuous learning, mentorship, and knowledge-sharing
• Contribute meaningfully as a core team member or DRI for a team or cross functional-project, leading and delivering work that contributes to advancing project goals
• Actively participate in hiring and developing talent in the team
• Solve complex, multi-step issues across systems independently, collaborating cross-functionally to deliver practical, scalable solutions
• Solve complex, multi-step issues that span multiple systems or processes, and help design solutions that reduce recurrence
• Collaborate across functions to address complex, sometimes ambiguous challenges, leveraging diverse inputs to enhance norms, products, processes, or services
• Balance creativity with feasibility to deliver practical, scalable solutions
• Apply a conceptual understanding of AI or ML and systems thinking to orchestrate AI microservices across core enterprise platforms, leveraging proficiency in data reasoning and context engineering to guide intelligent analysis and agile delivery
• Understand conceptually supervised or unsupervised learning, Large Language Models (LLMs), RAG architecture, and embeddings (Deep coding is not required, but architectural understanding is essential)
• Query and analyze data using SQL, and interpret statistical outputs, probabilities, and confidence intervals
• Map how AI microservices interact with core enterprise legacy systems (ERPs, CRMs) via REST APIs
• Structure enterprise context and business rules to guide AI-assisted analysis and pilot testing
• Utilize iterative lifecycle frameworks adapted for data science (e.g., CRISP-DM integrated with Agile or Scrum)

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