C2H UX Data Researcher

HTX Labs

2d•Remote

About The Position

This role operates at the intersection of UX research, AI workflows and evaluations, data systems architecture, immersive learning, analytics, and adaptive intelligence. You will help design the systems that allow EMPACT to continuously learn from user behavior, operational workflows, training outcomes, and AI-assisted interactions. You will work across immersive simulations, XR platforms, analytics pipelines, telemetry systems, AI-enabled workflows, and customer-driven operational environments to transform qualitative and quantitative user signal into structured intelligence that informs product evolution. This role is ideal for someone who thrives in fast-moving, evolving environments and is energized by combining systems thinking, human-centered design, AI-assisted research, analytics, and technical execution to solve complex problems.

Requirements

5+ years of experience in data analytics, data architecture, UX research, AI/ML analytics, learning analytics, or related fields.
3+ years of experience working with LRS, LMS, SCORM, xAPI products, learning data ecosystems, or training analytics platforms.
Hands-on experience using AI/ML, predictive analytics, generative AI tools, or AI-assisted research workflows to improve user insights, product analytics, or learning outcomes.
Bachelor’s degree in Computer Science, Data Science, Human-Computer Interaction, Cognitive Science, Information Systems, Learning Science, or a related field.
Experience with agile development processes and cross-functional product teams.
Must be authorized to work in the U.S.

Nice To Haves

Experience applying AI, machine learning, or generative AI in XR, simulation, training, education technology, defense, or enterprise learning environments.
Experience designing measurement frameworks for adaptive learning, intelligent tutoring, simulation-based training, or performance analytics.
Familiarity with Python, SQL, notebooks, BI tools, data warehouses, vector search, LLM workflows, or AI prototyping tools.
Experience evaluating AI-enabled features through usability testing, experimentation, model-output review, human-in-the-loop validation, or mixed-method research.

Responsibilities

Design, build, and maintain scalable data architectures that support immersive training XR applications, AI-enabled analytics, adaptive learning, and model-ready data pipelines.
Analyze large, multimodal datasets, including telemetry, user behavior, learning outcomes, xAPI/LRS data, qualitative research, and system performance data, to uncover trends, behaviors, and insights that inform product decisions and strategy.
Apply machine learning, predictive analytics, clustering, classification, natural language processing, and generative AI techniques to identify user patterns, forecast outcomes, and optimize learning experiences.
Collaborate with Product, Engineering, Design, and Learning teams to translate research, analytics, and AI findings into actionable recommendations, product requirements, experiments, and measurable improvements.
Develop dashboards, reports, prototypes, and visualization tools that make user behavior, learning effectiveness, AI model performance, and system performance clear to technical and non-technical stakeholders.
Integrate, manage, and evaluate analytics systems that monitor XR application performance, learner activity, user experience signals, and AI-enabled product features in real time.
Partner with stakeholders to define KPIs, success metrics, research questions, evaluation plans, and AI/product measurement frameworks.
Prototype and evaluate AI-assisted research workflows, including automated data coding, insight generation, survey analysis, usability synthesis, persona development, journey analysis, and research repository enrichment.
Research and evaluate emerging AI, data, analytics, and XR technologies to improve product intelligence, analytics capabilities, personalization, system efficiency, and user outcomes.
Support responsible AI practices by helping define data governance, model evaluation criteria, privacy safeguards, bias and risk considerations, explainability needs, and security compliance across data and AI systems.
Foster a culture of data-driven and AI-informed decision-making, experimentation, learning measurement, and continuous product improvement.