Objective

Identify and validate high-value AI opportunities, rapidly prototype solutions, and ensure implementations deliver measurable business outcomes and tangible ROI.

KPI

Revenue/cost impact of implemented AI solutions
Stakeholders' satisfaction with business case clarity and realization

Areas of Responsibility

Lead Discovery Process and Validate AI Opportunities
- Facilitate stakeholder workshops to identify and prioritize high-impact AI use cases
- Develop business cases with clear ROI models and success metrics
- Build consensus among stakeholders on solution direction
Map and Redesign Business Processes
- Document current workflows and pain points
- Design workflows with appropriate boundaries and controls for financial environments and Agentic AI systems
- Quantify expected business improvements
Define AI Solution Requirements
- Translate business needs into clear technical requirements
- Create user stories and acceptance criteria
- Establish validation approaches for measuring success
Create Solution Prototypes
- Build functional demonstrations using no-code/low-code tools
- Gather user feedback to refine concepts
- Develop prompt templates for financial use cases
Implement AI Governance and Compliance
- Develop testing methodologies for AI systems in regulated environments
- Ensure alignment with financial regulations (e.g US SR 11-7, EU AI Act)
- Create documentation standards for model risk management
Leverage Financial Services Industry (FSI) Ontologies
- Apply industry-standard financial ontologies (e.g. FIBO) to structure and ground data
- Design knowledge graph integrations to improve AI accuracy and compliance
- Validate ontology completeness and accuracy for financial applications
Ensure Ongoing Business Alignment
- Manage stakeholder expectations throughout delivery
- Lead business review sessions
- Mitigate risks to value realization
Stay Current on AI Capabilities
- Actively test and experiment with emerging AI technologies firsthand
- Evaluate new tools and platforms for business value
- Share relevant insights with stakeholders

Skills

Stakeholder management and workshop facilitation
Business process analysis, requirements gathering, and documentation
Project scoping, ROI modeling, and business case development
LLM frameworks and prompt engineering for financial applications
Hands-on AI tool experience, including no-code/low-code platforms
Financial ontologies and graph databases (Neo4j, RDF/OWL)
AI governance, testing, and validation in regulated environments

Traits

Strategic business thinker who can identify valuable AI application opportunities
Detail-oriented process analyst who can map complex workflows
Technically curious with practical, hands-on AI knowledge
Exceptional communicator who can translate between technical and business stakeholders
Creative problem-solver able to rapidly prototype solutions
Client-focused consultant who builds trust and drives business outcomes

Experience

3-5+ years experience in business analysis, process improvement, or consulting
Practical implementation experience with LLMs and generative AI
Hands-on experience with one or more no-code/low-code platforms
Hands-on experience with orchestration frameworks (LangChain, AutoGen) or knowledge graphs
Demonstrated success in requirements gathering and stakeholder management
Demonstrated success moving AI projects from proof-of-concept to production in regulated environments
Experience with digital transformation or technology implementation projects
Background in financial services with understanding of regulatory requirement

Terms & conditions

Full remote

Capacity: full-time

Time zone: Europe

Start date: April

Neurons Lab

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