Data Scientist - Applied AI & ML

About the role

Sunlighten is looking for a Data Scientist focused on applied AI and machine learning to help design, improve, validate, and scale AI-enabled products across Sales, Marketing, Customer Experience, Operations, Product, and BI. This is a hands-on, AI-first role centered on increasing the performance of current AI/ML capabilities and creating new solutions that deliver measurable business value. The work includes LLM agents, retrieval-augmented generation, semantic search, predictive modeling, forecasting, experimentation, and business analytics.

You will collaborate closely with the AI Applications Engineer, Data Engineering, BI, and business teams to turn ambiguous needs into dependable, secure, and measurable systems. The role involves prompt design, model and parameter selection, evaluation frameworks, retrieval tuning, knowledge-store design, monitoring, and continuous refinement of live AI workflows.

The position is broad enough to grow with Sunlighten’s AI strategy. While the main emphasis is AI and applied ML, the role also requires comfort with BI, analytics engineering, data modeling, and reporting support whenever business priorities call for it.

Sunlighten has spent 25 years building on its Kansas City roots and now serves a global market, including the UK. With the wellness industry expected to reach $7 trillion in 2026, the company positions itself at the center of a fast-growing health and longevity movement.

Location and work authorization

• This opportunity is only open to candidates who can legally work in the U.S. now and in the future without visa sponsorship.
• Applicants must already live in the Kansas City area, as only local candidates are being considered.

Key responsibilities

• Develop, test, and continuously improve AI products such as LLM agents, RAG experiences, semantic search tools, and decision-support applications.
• Work with the AI Applications Engineer on prompt design, prompt patterns, model choice, parameter tuning, tool-calling behavior, fallback handling, and user experience.
• Create and maintain evaluation systems for AI quality, including groundedness, usefulness, safety, completeness, consistency, and business relevance.
• Build golden datasets, expected-answer libraries, rubric-based scoring approaches, and regression tests for important AI use cases.
• Raise retrieval performance by improving chunking, metadata, embeddings, ranking, filtering, and knowledge-store structure.
• Support knowledge-store architecture, including Q&A layouts, metadata design, Cosmos DB considerations, semantic search patterns, and freshness rules for source content.
• Track AI system health across quality, response time, cost, drift, hallucination risk, escalation rate, user feedback, and business outcomes.
• Conduct red-team reviews, failure analysis, and quality assessments to reduce unsafe, inaccurate, or unsupported outputs.
• Document failure patterns, test outcomes, prompt/model versions, and follow-up improvement actions.
• Maintain and enhance machine learning models used for lead scoring, opportunity scoring, forecasting, and demand planning.
• Build additional predictive models for Sales, Marketing, CX, Operations, Product, and Finance use cases.
• Perform feature engineering using sources such as Salesforce, NetSuite, Five9, Shopify, Marketing Cloud, GA4, product telemetry, and other internal systems.
• Set metrics, thresholds, labels, holdout strategies, and retraining plans for ML models.
• Watch for model drift, performance decline, adoption issues, fairness concerns, and business impact changes.
• Convert model outputs into operational workflows like Salesforce scoring, routing, prioritization, dashboards, alerts, and automation rules.
• Explain assumptions, limitations, tradeoffs, and recommended actions to both technical and non-technical stakeholders.
• Work with stakeholders to turn business questions into measurable hypotheses and structured evaluation plans.
• Design and analyze experiments, including A/B tests, holdouts, quasi-experimental approaches, and pre/post comparisons.
• Define instrumentation needs before launch, such as events, identifiers, source systems, attribution logic, and guardrail metrics.
• Measure ROI through conversion lift, cost reduction, deflection, time savings, close rate, revenue impact, and operational efficiency.
• Prepare clear, decision-ready readouts that include recommendations, confidence levels, risks, and next steps.
• Support BI work when needed, including SQL analysis, Python notebooks, metric definitions, Power BI semantic model alignment, and dashboard assistance.
• 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 assets in Microsoft Fabric.
• Help validate data, investigate source systems, and troubleshoot issues across Salesforce, NetSuite, Five9, Shopify, Marketing Cloud, GA4, ClickHouse, Postgres, and related platforms.
• Contribute to reusable datasets, feature tables, semantic models, and governed metrics that support BI and AI.
• Maintain reproducible notebooks, scripts, model artifacts, prompts, evaluation outputs, and supporting documentation.
• Support version control for models, prompts, datasets, features, embeddings, and evaluation sets.
• Define release criteria for AI/ML solutions, including offline checks, safety validation, staging tests, canary rollout, and rollback conditions.
• Help implement automated checks for model quality, data quality, prompt regressions, retrieval quality, and production drift.
• Partner with engineering on CI/CD, APIs, monitoring, logging, alerts, and runbooks.
• Support incident reviews and root-cause analysis when AI/ML systems behave unexpectedly or produce poor outputs.
• Apply privacy-by-design thinking across AI, ML, and BI work.
• Limit exposure of personal data and ensure proper access controls, retention practices, and auditability.
• Follow least-privilege access principles and approved secret-management tools such as Azure Key Vault or 1Password.
• Ensure AI systems use approved data sources, documented retrieval logic, and human review where appropriate.
• Support auditable deletion, data retention, and compliance processes where needed.
• Perform additional duties as assigned.

Requirements

• 2 to 6 years of enterprise-level experience in applied data science, machine learning, AI, or analytics, with direct stakeholder-facing delivery.
• Bachelor’s or master’s degree in Data Science, Computer Science, Statistics, Operations Research, or comparable practical experience; a portfolio, GitHub profile, or shipped examples is preferred.
• Strong Python capability, including pandas, scikit-learn, notebooks, APIs, and production-ready scripting.
• Strong SQL skills and the ability to work with complex business data.
• Experience building, improving, or maintaining ML models such as classification, regression, forecasting, ranking, or anomaly detection.
• Understanding of LLM concepts such as prompting, embeddings, retrieval, RAG, semantic search, tool use, and model evaluation.
• Experience defining metrics, analyzing experiments, and explaining business impact.
• Ability to work through messy real-world data and translate analysis into practical business processes.
• Strong documentation practices and comfort with Git-based workflows.
• Strong communication skills and the ability to work directly with business stakeholders.
• Willingness to contribute to BI, analytics, and data engineering work when needed to achieve business outcomes.
• Preferred experience includes Microsoft Fabric, Lakehouse/Warehouse, Power BI semantic models, or Azure data tools.
• Preferred experience also includes Azure AI Foundry, Azure OpenAI, OpenAI API, or similar AI platforms.
• Additional preferred tools and platforms include vector search, semantic search, Cosmos DB, LangChain, LangGraph, and LlamaIndex.
• Experience with Salesforce, NetSuite, Five9, Shopify, Marketing Cloud, GA4, Gong, or customer/product telemetry is a plus.
• Experience creating AI/LLM evaluation frameworks with golden sets, rubric scoring, regression tests, and human review processes is preferred.
• MLOps or LLMOps experience, including monitoring, versioning, CI/CD, drift detection, and rollback planning, is also preferred.
• Familiarity with Grafana, Datadog, ClickHouse, Postgres, SQL Server, or similar observability/data platforms is beneficial.
• Experience translating ML or AI outputs into CRM, service, sales, marketing, operations, or product workflows is preferred.

Benefits and perks

• Collaborative, innovative work environment.
• Career growth opportunities within a market-leading wellness technology company that is expanding quickly.
• Competitive paid time off, paid holidays, and floating holidays.
• On-site fully equipped fitness center.
• Lunch program featuring a James Beard Award-winning chef.
• Health insurance with HSA and FSA options, plus dental and vision coverage.
• 401(k) plan with company contributions.
• Profit sharing.
• Life insurance and short-term disability coverage.
• Professional development support and tuition reimbursement.
• Associate discounts on saunas, spa products, and day spa services.

Equal opportunity statement

Sunlighten is committed to equal employment opportunity and affirmative action. Employment decisions are made without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability, protected veteran status, or any other characteristic protected by applicable federal, state, or local law.