Data Scientist - Applied AI & ML

**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, Applied AI & ML to help build, improve, evaluate, and scale AI and machine learning products across Sales, Marketing, Customer Experience, Operations, Product, and BI.  This is an AI-first applied data science role and the primary focus is improving existing AI/ML capabilities and developing new AI-powered products that create measurable business impact. This includes LLM agents, RAG systems, semantic search, predictive models, forecasting, experimentation, and business-facing analytics. 

You will partner closely with the AI Applications Engineer, Data Engineering, BI, and business stakeholders to turn ambiguous business problems into reliable, secure, measurable solutions. You will work on system prompts, model selection, model parameters, evaluation frameworks, retrieval quality, knowledge-store design, monitoring, and continuous improvement of production AI workflows. 

This role is intentionally broad enough to evolve with Sunlighten’s AI roadmap. While the primary focus is AI and applied ML, the person in this role should be comfortable supporting BI, analytics engineering, data modeling, and reporting needs when business priorities require it. 

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.

Duties/Responsibilities:

• Applied AI, LLMs, and Agent Quality 
• Build, evaluate, and improve AI-powered products, including LLM agents, RAG workflows, semantic search experiences, and decision-support tools  
• Partner with the AI Applications Engineer on system prompts, prompt patterns, model selection, model parameters, tool-calling behavior, fallback logic, and user experience  
• Design and maintain evaluation frameworks for AI systems, including groundedness, helpfulness, safety, completeness, consistency, and business usefulness  
• Build and maintain golden datasets, expected-answer sets, rubric-based scoring, and regression tests for key AI use cases  
• Improve retrieval quality through better chunking, metadata, embeddings, ranking, filtering, and knowledge-store design  
• Support knowledge-store architecture, including Q&A structures, metadata schema, Cosmos DB design considerations, semantic search patterns, and source freshness rules  
• Monitor AI systems for quality, latency, cost, drift, hallucination risk, escalation rate, user feedback, and business outcomes  
• Run red-team testing, failure analysis, and quality reviews to reduce unsafe, inaccurate, or ungrounded responses  
• Document known failure modes, evaluation results, model/prompt versions, and improvement plans  
• Machine Learning and Predictive Modeling 
• Own and improve existing ML models used by the business, including lead scoring, opportunity scoring, forecasting, and demand planning  
• Develop new predictive models as needed for Sales, Marketing, CX, Operations, Product, and Finance use cases  
• Perform feature engineering across systems such as Salesforce, NetSuite, Five9, Shopify, Marketing Cloud, GA4, product telemetry, and other internal data sources  
• Define model metrics, business success metrics, thresholds, labels, holdout sets, and retraining strategies  
• Monitor models for drift, degradation, adoption, fairness, and business impact  
• Translate model outputs into business workflows such as Salesforce scoring, routing, prioritization, dashboards, alerts, and automation rules  
• Explain model assumptions, limitations, tradeoffs, and recommended actions to technical and non-technical audiences  
• Experimentation, Measurement, and Business Impact 
• Partner with stakeholders to convert business questions into testable hypotheses, success metrics, and measurement plans  
• Design and analyze experiments, including A/B tests, holdouts, quasi-experimental designs, and pre/post me