Data Scientist - Applied AI & ML

Sunlighten•Leawood, KS

11h•Onsite

About The Position

At Sunlighten, 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.

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 Haves

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

Responsibilities

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
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
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
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
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
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.