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    "requisitionDescription": "<div><div>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:<br data-pasted=\"true\"><ul><ul><li><strong>Integrity.</strong>&nbsp; We act in the best interests of others by providing an honest, consistent experience for our clients and team.&nbsp;</li><li><strong>Passion</strong>. We pursue our full potential, seeking to continually enhance and evolve our ability to serve our clients and team.</li><li><strong>Teamwork.&nbsp;</strong>We subordinate our egos to work together for the benefit of our clients.&nbsp;</li></ul></ul>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.<br><br><p><strong data-pasted=\"true\"><span style=\"--original-color: #1d1c1d; color: rgb(29, 28, 29); font-size: 18px;\"><u>Summary of the position</u></span></strong><span style=\"--original-color: #1d1c1d;color: #1d1c1d;\"><span style=\"font-size: 18px;\"><u><br></u></span>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.</span></p><p><span style=\"--original-color: #1d1c1d;color: #1d1c1d;\">This role sits at the intersection of AI/ML innovation, process optimization, rapid prototyping, and cross-department collaboration &mdash; ensuring that AI-first solutions are discovered, validated, and iterated to deliver clear, quantifiable value aligned with Sequoia&#39;s mission to redefine the client experience through data-driven insights and innovative technology.</span></p><p><strong><span style=\"--original-color: #1d1c1d; color: rgb(29, 28, 29); font-size: 18px;\"><u>Responsibilities</u></span></strong></p><ul><li>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</li><li>Conduct user interviews, field observations, and journey walkthroughs to surface real problems behind stated requests; apply Jobs-To-Be-Done and behavioral insights</li><li>Create a Business Case Repository with ROI and Value Metrics to justify and rank use cases for AI-first Transformation</li><li>Convert qualitative insights into quantified problem definitions with baselines, success metrics, constraints, and value hypotheses</li><li>Build opportunity canvases and prioritization frameworks (impact, effort, risk, time-to-value) to focus on the most valuable use cases</li><li>Map current-state processes, identify pain points, and quantify improvement opportunities (cycle time, throughput, quality, cost)</li><li>Design future-state workflows considering AI/LLM augmentation, straight-through processing, and exception handling</li><li>Manage intake for new processes, automation requests, and optimization initiatives</li><li>Coordinate discovery workshops, kaizen events, and lessons-learned sessions with stakeholders</li><li>Design and build POCs using LLMs, retrieval-augmented generation (RAG), prompt engineering, RPA, and classical ML to validate feasibility quickly</li><li>Evolve POCs into MVPs with clear functional boundaries, guardrails, and success criteria; prepare handoffs for engineering or vendor buildout</li><li>Run short discovery sprints with user testing, A/B or champion&ndash;challenger evaluations, and weekly feedback loops</li><li>Maintain a transparent backlog and roadmap; time-box experiments and enforce clear &quot;continue/pivot/stop&quot; gates</li><li>Partner with leaders in Client Experience, Operations, Compliance, Planning, and Marketing to stand up shared automation and optimization initiatives</li><li>Align process definitions and business rules with the Data Dictionary and Data Governance artifacts to ensure consistency</li><li>Embed responsible-AI principles, data privacy, RBAC, and human-in-the-loop controls; escalate model/LLM limitations and biases with mitigations</li><li>Coordinate with Legal/Compliance on acceptable use, content controls, and auditability</li><li>Create decision playbooks, process guides, and workflow aids that make AI outputs usable by front-line teams</li><li>Lead show-and-tell sessions and office hours to grow an optimization culture and improve intake quality</li><li>Coordinate with vendors and internal engineering on process automation options, integration approaches, and build-vs-buy decisions</li><li>Align deliverables with PMO milestones, capacity constraints, and Definition of Done</li></ul><p><strong><span style=\"--original-color: #1d1c1d; color: rgb(29, 28, 29); font-size: 18px;\"><u>Required Skills/Experience</u></span></strong></p><ul><li>Bachelor&#39;s degree required; Master&#39;s degree preferred</li><li>8&ndash;12 years in AI/ML, process optimization, automation, analytics, or a similar problem-solving role</li><li>Demonstrated passion for process improvement and human-centered discovery; ability to translate ambiguous needs into quantified, testable hypotheses</li><li>Hands-on experience building POCs with LLMs (prompt engineering, evaluation, RAG) and ML models in Python; comfort with notebooks, version control, and experiment tracking</li><li>Proficiency in business process modeling, value stream mapping, and workflow optimization techniques (Lean, Six Sigma)</li><li>Strong communication skills with the ability to dive deep with users and influence executives using crisp narratives and visuals</li><li>Familiarity with Data Governance concepts, Data Dictionary, and privacy/RBAC guardrails</li><li>Experience working in iterative, product-centric delivery models with short feedback cycles</li></ul><p><strong><span style=\"--original-color: #1d1c1d; color: rgb(29, 28, 29); font-size: 18px;\"><u>Preferred Skills/Experience</u></span></strong></p><ul><li>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</li><li>Practical knowledge of LLM tooling (prompt templates, vector stores, retrieval pipelines), RPA platforms, classical ML, and evaluation frameworks</li><li>Comfort with BI and communication via Tableau/Power BI for decision enablement</li><li>Experience facilitating workshops, design sprints, or JTBD interviews</li></ul><p><strong><span style=\"--original-color: #1d1c1d; color: rgb(29, 28, 29); font-size: 18px;\"><u>Competencies</u></span></strong></p><ul><li><strong>Discovery Mindset</strong> &mdash; Surfaces real problems behind stated requests using human-centered methods and behavioral insights</li><li><strong>Analytical Rigor</strong> &mdash; Converts qualitative insights into quantified problem definitions with baselines, success metrics, and value hypotheses</li><li><strong>Rapid Experimentation</strong> &mdash; Builds POCs and MVPs quickly, enforcing clear continue/pivot/stop gates to de-risk solutions before scale</li><li><strong>Collaboration</strong> &mdash; Partners across Client Experience, Operations, Compliance, Planning, Marketing, and vendor stakeholders to drive shared outcomes</li><li><strong>Accountability</strong> &mdash; Owns the end-to-end journey from idea &rarr; POC &rarr; MVP with measurable business value</li><li><strong>Adaptability</strong> &mdash; Thrives in experimentation, iterative development, and fast-moving AI landscapes; comfortable learning from failed experiments and pivoting quickly</li></ul></div></div>\n",
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