AI Discovery Specialist

AI Discovery Specialist

Sequoia Financial Group is a growing Registered Investment Advisor (RIA), headquartered in Northeast Ohio, offering financial planning and wealth management services. At Sequoia, we exist with a singular purpose: to enrich lives. Our values define how we behave and guide us through the pursuit of our purpose to enrich lives. At Sequoia, our core values are:

• Integrity. We act in the best interests of others by providing an honest, consistent experience for our clients and team.
• Passion. We pursue our full potential, seeking to continually enhance and evolve our ability to serve our clients and team.
• Teamwork. We subordinate our egos to work together for the benefit of our clients.

Our promise to team members is that you will grow with us. From experienced advisors to new college grads to transitioning principals, every team member will find Sequoia a place to refine their professional mission, move into new opportunities, go deeper, and lead further. We are built to help you build a career here as a long-term contributor in our work to enrich lives for generations.

The AI Discovery Specialist is a strategic and hands-on role responsible for leading enterprise-wide AI discovery to identify, frame, and prioritize high-impact business problems that can be solved with AI, LLMs, and Data Science. This individual will analyze and optimize end-to-end business processes, build rapid proofs of concept, and drive automation initiatives that enhance personalization, operational efficiency, and strategic decision-making across Sequoia Financial Group.

This role sits at the intersection of AI/ML innovation, process optimization, rapid prototyping, and cross-department collaboration — ensuring that AI-first solutions are discovered, validated, and iterated to deliver clear, quantifiable value aligned with Sequoia's mission to redefine the client experience through data-driven insights and innovative technology.

Responsibilities:

• Lead enterprise-wide AI discovery to identify, frame, and prioritize high-impact business problems solvable with AI, LLMs, and Data Science, with clear, quantifiable value
• Conduct user interviews, field observations, and journey walkthroughs to surface real problems behind stated requests; apply Jobs-To-Be-Done and behavioral insights
• Create a Business Case Repository with ROI and Value Metrics to justify and rank use cases for AI-first Transformation
• Convert qualitative insights into quantified problem definitions with baselines, success metrics, constraints, and value hypotheses
• Build opportunity canvases and prioritization frameworks (impact, effort, risk, time-to-value) to focus on the most valuable use cases
• Map current-state processes, identify pain points, and quantify improvement opportunities (cycle time, throughput, quality, cost)
• Design future-state workflows considering AI/LLM augmentation, straight-through processing, and exception handling
• Manage intake for new processes, automation requests, and optimization initiatives
• Coordinate discovery workshops, kaizen events, and lessons-learned sessions with stakeholders
• Design and build POCs using LLMs, retrieval-augmented generation (RAG), prompt engineering, RPA, and classical ML to validate feasibility quickly
• Evolve POCs into MVPs with clear functional boundaries, guardrails, and success criteria; prepare handoffs for engineering or vendor buildout
• Run short discovery sprints with user testing, A/B or champion–challenger evaluations, and weekly feedback loops
• Maintain a transparent backlog and roadmap; time-box experiments and enforce clear "continue/pivot/stop" gates
• Partner with leaders in Client Experience, Operations, Compliance, Planning, and Marketing to stand up shared automation and optimization initiatives
• Align process definitions and business rules with the Data Dictionary and Data Governance artifacts to ensure consistency
• Embed responsible-AI principles, data privacy, RBAC, and human-in-the-loop controls; escalate model/LLM limitations and biases with mitigations
• Coordinate with Legal/Compliance on acceptable use, content controls, and auditability
• Create decision playbooks, process guides, and workflow aids that make AI outputs usable by front-line teams
• Lead show-and-tell sessions and office hours to grow an optimization culture and improve intake quality
• Coordinate with vendors and internal engineering on process automation options, integration approaches, and build-vs-buy decisions
• Align deliverables with PMO milestones, capacity constraints, and Definition of Done

Required Skills/Experience:

• Bachelor's degree required; Master's degree preferred
• 8–12 years in AI/ML, process optimization, automation, analytics, or a similar problem-solving role
• Demonstrated passion for process improvement and human-centered discovery; ability to translate ambiguous needs into quantified, testable hypotheses
• Hands-on experience building POCs with LLMs (prompt engineering, evaluation, RAG) and ML models in Python; comfort with notebooks, version control, and experiment tracking
• Proficiency in business process modeling, value stream mapping, and workflow optimization techniques (Lean, Six Sigma)
• Strong communication skills with the ability to dive deep with users and influence executives using crisp narratives and visuals
• Familiarity with Data Governance concepts, Data Dictionary, and privacy/RBAC guardrails
• Experience working in iterative, product-centric delivery models with short feedback cycles

Preferred Skills/Experience:

• Background in financial services/wealth advisory and familiarity with Salesforce and planning/portfolio platforms (e.g., Tamarac, Orion, Addepar, Black Diamond); exposure to eMoney, custodians, and Box is a plus
• Practical knowledge of LLM tooling (prompt templates, vector stores, retrieval pipelines), RPA platforms, classical ML, and evaluation frameworks
• Comfort with BI and communication via Tableau/Power BI for decision enablement
• Experience facilitating workshops, design sprints, or JTBD interviews

Competencies:

• Discovery Mindset — Surfaces real problems behind stated requests using human-centered methods and behavioral insights
• Analytical Rigor — Converts qualitative insights into quantified problem definitions with baselines, success metrics, and value hypotheses
• Rapid Experimentation — Builds POCs and MVPs quickly, enforcing clear continue/pivot/stop gates to de-risk solutions before scale
• Collaboration — Partners across Client Experience, Operations, Compliance, Planning, Marketing, and vendor stakeholders to drive shared outcomes
• Accountability — Owns the end-to-end journey from idea → POC → MVP with measurable business value
• Adaptability — Thrives in experimentation, iterative development, and fast-moving AI landscapes; comfortable learning from failed experiments and pivoting quickly