As an AI Cloud Data Scientist, you will decompose business processes with line-of-business partners, determine whether the right answer is an AI agent, a machine learning model, a deterministic rule, or a combination, and build the solution far enough to prove its value. You will also build the reusable tools, patterns, and evaluation practices that make the broader data science team more capable, working closely with a Principal Data Scientist and partner engineering teams. This role owns problem framing, solution design, prototyping, evaluation, and production-ready handoff. Partner engineering teams own production deployment, scaling, and operations unless otherwise assigned. Overall responsibilities which will include multiple initiatives assigned by Data Science and IT leadership.

This role is hybrid (Mon through Thu on-site / Fri remote) for candidates in the Kansas City metropolitan area and open to qualified remote candidates outside of the Kansas City area. However, the remote location must be within the US.

How you'll spend your time:

• Agentic AI & LLM Solution Development:
  • Design and build multi-step agentic AI systems-tool and function calling, orchestration, retrieval, and memory-on AWS Bedrock Agents or an equivalent platform.
  • Engineer prompts and context, including the business and semantic context agents need to accurately answer natural-language questions from the business.
  • Prototype, iterate, and prove solutions in a development environment, and apply large language models to automate, augment, and replace manual workflows.
• Business Translation & Partnership:
  • Partner with business teams to decompose processes, identify outdated systems and manual work that are strong candidates for AI modernization, and choose the simplest method that solves the problem well.
  • Solve the underlying problem rather than putting a patch on it, own the solution from discovery through validated results, and communicate tradeoffs and outcomes clearly to business and technical audiences.
• Machine Learning, Evaluation & Governance:
  • Build, evaluate, and reason about machine learning models end to end when the problem calls for it, applying sound experimentation, feature engineering, and validation practices.
  • Build evaluation harnesses and define metrics to measure agent and model quality, including failure analysis and regression testing.
  • Implement guardrails, human-in-the-loop checks, and the security, compliance, and governance controls a regulated banking environment requires.
• AI Enablement & Handoff to Engineering:
  • Build reusable skills, workflows, tools, and patterns-for prompts, context, tool interfaces, evaluations, and approvals-that improve data science productivity and quality across the team, and share them through examples and mentoring.
  • Define data, pipeline, and integration requirements, and hand off solutions to engineering with documented designs, evaluation results, and acceptance criteria.

In your first year, success looks like a high-friction bank process turned into a governed agentic workflow with measurable value, and a reusable evaluation approach that other initiatives adopt.

We're excited to talk if you have:

• At least 3 years of experience across data science, machine learning, AI engineering, applied research, or enterprise software development, plus a Bachelor's degree in a related field or an equivalent combination of education and experience. We value demonstrated ability and project depth over an exact title or degree path.
• Strong proficiency in Python and SQL, solid machine learning skills, and work with normal engineering discipline: Git, testing, documentation, and APIs.
• Built at least one multi-step LLM workflow involving tool or function calling, orchestration, retrieval, structured outputs, state, or human approval paths.
• Ability to evaluate AI and model outputs using repeatable tests, meaningful quality metrics, and failure analysis.
• Ability to translate ambiguous business problems into AI solutions, scope them with stakeholders, and explain tradeoffs to non-technical audiences.

Bonus Points if you have:

• Built AI or machine learning solutions in AWS (Bedrock, SageMaker, Lambda) or comparable cloud AI services.
• Worked in financial services or another regulated industry.
• Developed semantic views or semantic layers to provide business context for analytics or AI.
• Built internal AI tools or reusable capabilities that make a technical team faster.

We welcome candidates from applied science, machine learning engineering, analytics engineering, software engineering, and product-facing data science backgrounds-what matters is demonstrated ability, not an exact prior title.

Applicants must have legal authority to work in the United States. Work Visa sponsorship is not available for this position.

This position was posted on 09/16/2026. The application deadline for this position is 45 days from the posting date unless filled earlier.

Compensation Range:
$98,208 - $144,705

The posted compensation range on this listing represents UMB's good faith and reasonable estimate based on its budget and what it expects to be the starting pay for this role, but the actual compensation may vary by geographic location, experience level, and other job-related factors. Please see the description of benefits included with this job posting for additional information.

UMB offers competitive and varied benefits to eligible associates, such as Paid Time Off; a 401(k) matching program; annual incentive pay; paid holidays; a comprehensive company sponsored benefit plan including medical, dental, vision, and other insurance coverage; health savings, flexible spending, and dependent care accounts; adoption assistance; an employee assistance program; fitness reimbursement; tuition reimbursement; an associate wellbeing program; an associate emergency fund; and various associate banking benefits. Benefit offerings and eligibility requirements vary.

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