Senior Data Scientist

Sequoia Financial Group Llc•Mayfield Heights, OH

About The Position

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. As we expand our Data & AI Office, we seek a hands-on Senior Data Scientist to help shape our data and AI strategy, drive architectural excellence, and enable scalable, secure, and intelligent AI-first processes. This role is pivotal in supporting our enterprise-wide AI initiatives and AI adoption. ROLE OVERVIEW Lead enterprise-wide AI discovery and predictive modeling to identify high-impact business problems and translate them into production-ready solutions. Build Proof of Concepts (POCs) and Minimal Viable Products (MVPs) using AI/LLMs and data science, operationalize predictive models across Client Experience, Operations, Compliance, and Marketing, and drive a culture of experimentation grounded in measurable ROI and responsible AI.

Requirements

7–12 years in data science, ML, analytics, or product discovery
Python expertise: pandas, scikit-learn, NumPy; Jupyter notebooks and Git
Statistical & ML fundamentals: feature engineering, validation, error analysis
LLM & AI proficiency: POC development with prompt engineering, RAG, and classical ML
Decision-support framing: translate models into actionable workflows; define KPIs and measure business impact
Data governance: Data Dictionary, lineage, RBAC, privacy principles
Communication: strong written/verbal skills with technical and non-technical audiences
Iterative delivery: comfort with short feedback cycles, A/B testing, and learning from experiments

Responsibilities

Conduct user interviews and journey walkthroughs to surface real problems; apply behavioral insights to translate ambiguous needs into quantified problem statements and value hypotheses.
Build prioritization frameworks (impact, effort, risk, data readiness, compliance) and size ROI for use cases.
Partner with Finance/PMO to track realized value vs. forecast post-launch
Design POCs using LLMs, RAG, prompt engineering, and classical ML; evolve into MVPs with clear success criteria and guardrails.
Conduct feature engineering, algorithm selection, and set monitoring plans for drift and bias
Package prototypes with evaluation harnesses; enforce clear "continue/pivot/stop" decision gates
Build and maintain forecasting and propensity models (churn, next best action, AUM growth, advisor capacity, client lifecycle scoring) serving multiple departments.
Standardize feature stores across Salesforce and planning/portfolio platforms (Tamarac, Orion, Addepar, Black Diamond)
Define SLAs/SLOs, feature refresh cadence, and rollback criteria
Convert model outputs into actionable artifacts, such as decision playbooks, scenario calculators, dashboards, and alerting rules.
Define workflows, leading indicators, and counter-metrics that drive business actions.
Create Tableau/Power BI visualizations with clear narratives for non-technical stakeholders.
Champion MLOps hygiene: versioning, experiment tracking, model cards, and documentation
Host office hours and workshops to build data literacy and ethical AI awareness
Embed responsible-AI principles, RBAC, and human-in-the-loop controls; coordinate with Legal/Compliance on risk and auditability
Rigorously document Workflows and Processes to establish the AI and Data Office for long-term reliability and resilience
Maintain a transparent inventory of models and experiments; deprecate low-value assets.
Establish prioritization scoring frameworks and publish quarterly roadmap updates.
Track portfolio health: use-case progression, realized value, time-to-decision improvements
Coordinate with data engineering on operationalization and with analytics on KPI alignment.
Collaborate with PMO on experiment time-boxing and capacity management
Work with vendors on architecture, integration, and build-vs-buy decisions