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 measurement

Define instrumentation requirements before launch, including events, IDs, source systems, attribution logic, and guardrail metrics

Quantify ROI using metrics such as conversion lift, cost savings, deflection, time saved, close rate, revenue impact, and operational efficiency

Produce decision-ready readouts with clear recommendations, confidence levels, risks, and next steps

BI, Analytics, and Data Engineering Support

Support BI and analytics work when needed, including SQL analysis, Python notebooks, metric definitions, Power BI semantic model alignment, and dashboard support

Help improve data quality, lineage, documentation, and metric consistency across BI and AI workflows

Partner with Data Engineering to productionize datasets, features, pipelines, and AI-ready data assets in Microsoft Fabric

Assist with data validation, source-system analysis, and troubleshooting across Salesforce, NetSuite, Five9, Shopify, Marketing Cloud, GA4, ClickHouse, Postgres, and other systems

Contribute to reusable datasets, feature tables, semantic models, and governed metrics that support both BI and AI use cases

MLOps, LLMOps, and Production Readiness

Maintain reproducible notebooks, scripts, model artifacts, prompts, evaluation results, and documentation

Support versioning for models, prompts, datasets, features, embeddings, and evaluation sets

Define release gates for AI/ML systems, including offline evaluations, safety checks, staging validation, canary testing, and rollback criteria

Implement or support automated checks for model quality, data quality, prompt regressions, retrieval quality, and production drift

Partner with engineering on CI/CD, APIs, monitoring, logging, alerting, and operational runbooks

Support incident review and root-cause analysis when AI/ML systems produce unexpected or low-quality outcomes

Governance, Privacy, and Security

Apply privacy-by-design principles across AI, ML, and BI work

Minimize PII exposure and ensure appropriate access controls, retention rules, and auditability

Follow least-privilege access standards and approved secret-management practices such as Azure Key Vault or 1Password

Ensure AI systems use approved data sources, documented retrieval logic, and appropriate human-in-the-loop review where needed

Support auditable deletion, data retention, and compliance processes where applicable

Other duties as discussed and assigned.

Requirements

2–6 years of enterprise level experience in applied data science, machine learning, AI, or analytics with stakeholder-facing delivery.

Bachelors or Masters degree in Data Science, Computer Science, Statistics, Operations Research (or equivalent practical experience); portfolio, GitHub or examples of shipped work preferred.

Strong Python skills, including pandas, scikit-learn, notebooks, APIs, and production-oriented scripting

Strong SQL skills and ability to work across complex business datasets

Experience building, improving, or maintaining machine learning models such as classification, regression, forecasting, ranking, or anomaly detection

Familiarity with LLM concepts such as prompting, embeddings, retrieval, RAG, semantic search, tool use, and model evaluation

Experience defining metrics, analyzing experiments, and communicating business impact

Ability to work with messy real-world data and translate analysis into practical business workflows

Strong documentation habits and comfort with Git-based workflows

Strong communication skills and ability to work directly with business stakeholders

Willingness to support BI, analytics, and data engineering work when needed to deliver business outcomes

Nice to Have (Preferred Experience)

Experience with Microsoft Fabric, Lakehouse/Warehouse, Power BI semantic models, or Azure data tools

Experience with Azure AI Foundry, Azure OpenAI, OpenAI API, or similar AI platforms

Experience with vector search, semantic search, Cosmos DB, LangChain, LangGraph, LlamaIndex, or similar tools

Experience with Salesforce, NetSuite, Five9, Shopify, Marketing Cloud, GA4, Gong, or customer/product telemetry

Experience building evaluation frameworks for AI/LLM systems, including golden sets, rubric scoring, regression tests, and human review workflows

Experience with MLOps or LLMOps practices, including monitoring, model/prompt versioning, CI/CD, drift detection, and rollback plans

Experience with Grafana, Datadog, ClickHouse, Postgres, SQL Server, or similar observability/data platforms

Experience translating ML or AI outputs into CRM, service, sales, marketing, operations, or product workflows

Benefits

Opportunity to work in a collaborative and innovative environment.

Career growth opportunities in a market leading and rapidly growing wellness technology company.

Competitive Paid Time Off Policy + Paid Holidays + Floating Holidays.

Fully Equipped Fitness Center On-Site.

Lunch Program featuring a James-Beard Award Winning Chef.

Health (HSA & FSA Options), Dental, and Vision Insurance.

401(k) with company contributions.

Profit Sharing.

Life and Short-Term Disability Insurance.

Professional Development and Tuition Reimbursement.

Associate Discounts on Saunas, Spa Products and Day Spa Services.