AI Discovery Specialist

Sequoia Financial Group Llc•Cleveland, OH

21h

About The Position

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-departmental 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.

Requirements

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

Nice To Haves

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

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