Data Scientist - AI & BI

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* Please Note:

This position is open only to candidates authorized to work in the U.S. without the need for current or future visa sponsorship. Additionally, this position is based in the Kansas City area, and we are only considering candidates who reside locally.**

At Sunlighten, we're not just about infrared saunas, we’re on a mission to improve lives through innovative health and wellness solutions. As a global leader in infrared sauna therapy, we are rapidly expanding and need a talented Data Scientist, AI & BI to drive measurable impact across Sales, Marketing, CX, and Operations through applied ML/LLMs, experimentation, and analytics. This is an AI-first role: you will own evaluation, monitoring, and continuous improvement for AI agents and RAG experiences, and partner with our AI Applications Engineer to product ionize safe, reliable workflows that are accurate, secure, and ROI-positive.

You will also lead core BI data science work (forecasting, scoring, experimentation) and step into BI analytics/dashboarding as needed to ensure business priorities ship end to end.

Celebrating 25 years of innovation, Sunlighten has grown from its Kansas City roots to establish a global footprint, including expansion into the UK. With the global wellness market projected to reach $7 trillion in 2026, we are proud to be part of this dynamic and holistic shift. As leaders in light science and longevity, we create innovative solutions that help customers lead vibrant, active lifestyles.

• Define evaluation strategy for LLM/RAG/agents: grounded, helpfulness, safety, regression tests, and release gates.
• Build and maintain “golden sets” and rubric-based scoring for agent behavior across key use cases.
• Establish monitoring for agent outcomes: quality, latency, cost, drift, user feedback, and business KPIs.
• Partner with the AI Applications Engineer on prompt strategy, retrieval patterns, tool-use behavior, and safe fallbacks.
• Run red team/adversarial testing and coordinate mitigations for unsafe or ungrounded behavior.
• Ensure privacy/security by design: PII minimization, RBAC/least privilege, secrets via Key Vault/1
  
  Password, auditable deletions (≤7 days where applicable).
• Define human in the loop workflows when needed (sampling, review queues, labeling guidelines, escalation paths).

• Own an LLM release process: prompt/model/versioning, offline evals, staging, canary, and rollback.
• Maintain documentation for production AI: evaluation reports, model/prompt “cards,” known failure modes, and mitigation playbooks.
• Implement automated regression checks (pre/post) to prevent quality/safety backslides.
• Define incident response expectations for agent issues: triage, root cause analysis, corrective actions, and follow-up measurement.

• Partner with stakeholders (Sales/Marketing/CX/Ops) to convert questions into testable plans, success metrics, and decision ready recommendations.
• Own predictive modeling for BI priorities: lead/opportunity scoring, demand planning, end-to-end forecasting, and product/website models as needed.
• Design and run experiments (A/B, holdouts, quasi experimental when needed): power, guardrails, instrumentation, readouts.
• Define; build golden labels/holdouts; quantify ROI and operationalize decision thresholds.
• Feature engineering across Salesforce, Net Suite, Five9, Marketing Cloud, Shopify, GA4, and product telemetry; collaborate with Data Engineering to product ionize in Microsoft Fabric.
• Translate modeling outputs into operational workflows (e.g., Salesforce scoring, routing, prioritization, dashboards, and alerts).
• This is an AI-first role, but you’re expected to pitch in on BI/analytics work when priorities demand it (metric definitions, semantic model alignment, dashboards, and executive readouts) to ensure outcomes land not just models.
• Improve data clarity: metric definitions, data quality checks, lineage notes, and stakeholder enablement.
• Other duties as discussed and assigned.

• 2–6 years of enterprise level experience in applied data science or analytics with stakeholder-facing…