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About This Role
\*\*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.
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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.
Sunlighten provides equal employment opportunity. Discrimination of any type will not be tolerated. Sunlighten is an Equal Opportunity / Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability, protected veteran status or any other characteristic protected by state, federal, or local law.
Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 3,823 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Sunlighten, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $198,000 based on 808 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Sunlighten AI Hiring
Sunlighten has 1 open AI role right now. They're hiring across Data Scientist. Based in Leawood, KS, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
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