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
