C2H UX Data Researcher

📍 Job Overview

Job Title: C2H UX Data Researcher

Company: HTX Labs

Location: Remote (United States)

Job Type: CONTRACTOR (Contract-to-Hire)

Category: Data & Analytics / Technology / UX Research

Date Posted: May 12, 2026

Experience Level: Mid-Senior Level (5+ years experience)

Remote Status: Fully Remote

🚀 Role Summary

• This role bridges UX research, data systems architecture, AI workflows, and immersive learning technologies, focusing on building intelligent systems that learn from user behavior and operational data.

• Key responsibilities include designing and maintaining scalable data architectures for XR applications, analyzing complex multimodal datasets, and applying AI/ML techniques to derive actionable insights.

• The position requires proficiency in synthesizing qualitative and quantitative data, understanding user behavior in immersive environments, and collaborating with cross-functional teams to drive product evolution.

• This is an opportunity to leverage AI-assisted research and advanced analytics to enhance user experiences within XR and adaptive learning platforms, with a strong emphasis on responsible AI practices.

📝 Enhancement Note: The "C2H" designation indicates a Contract-to-Hire opportunity, suggesting that performance during the contract period will be evaluated for potential conversion to a permanent role. This implies a need for strong performance, adaptability, and alignment with company culture and long-term objectives. The role's focus on "AI-assisted research" and "generative AI" suggests a forward-thinking company actively integrating cutting-edge technologies into its research and development processes.

📈 Primary Responsibilities

• Design, build, and maintain scalable data architectures that support immersive XR training applications, AI-enabled analytics, adaptive learning systems, and model-ready data pipelines.

• Analyze large, multimodal datasets, including telemetry, user behavior, learning outcomes, xAPI/LRS data, qualitative research, and system performance, to identify trends and inform product strategy.

• Apply machine learning, predictive analytics, clustering, classification, Natural Language Processing (NLP), and Generative AI techniques to understand 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, experimental designs, and measurable improvements.

• Develop dashboards, reports, prototypes, and visualization tools to clearly communicate user behavior, learning effectiveness, AI model performance, and system performance to both 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 Key Performance Indicators (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 enhance product intelligence, analytics capabilities, personalization, system efficiency, and user outcomes.

• Support responsible AI practices by contributing to 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.

📝 Enhancement Note: The responsibilities highlight a demanding role requiring a blend of technical expertise in data architecture and AI, analytical rigor in data interpretation, and user-centricity in UX research. The emphasis on "AI-assisted research workflows" and "responsible AI practices" indicates a forward-looking approach to research and development, requiring candidates to be adept at both leveraging and critically evaluating AI tools.

🎓 Skills & Qualifications

Education:

Experience:

• 5+ years of progressive experience in data analytics, data architecture, UX research, AI/ML analytics, learning analytics, or related fields.

• 3+ years of hands-on experience working with Learning Record Stores (LRS), Learning Management Systems (LMS), SCORM, xAPI products, learning data ecosystems, or training analytics platforms.

• Proven experience using AI/ML, predictive analytics, generative AI tools, or AI-assisted research workflows to enhance user insights, product analytics, or learning outcomes.

• Experience with agile development processes and working within cross-functional product teams.

Required Skills:

• Strong experience with data analysis tools, visualization platforms, analytics workflows, and AI-assisted research methods.

• Proficiency in database design, data modeling, management, querying, and preparation of high-quality datasets for analytics and machine learning.

• Knowledge of cloud-based data architecture, AI/ML infrastructure concepts, data pipelines, APIs, and integration patterns.

• Familiarity with analytics frameworks and tools, including learning analytics, product analytics, telemetry, LRS, LMS, SCORM, xAPI, and experimentation platforms.

• Experience with machine learning and predictive analytics techniques, including supervised and unsupervised learning, NLP, model evaluation, and applied generative AI use cases.

• Ability to responsibly use AI tools to accelerate research synthesis, insight discovery, data exploration, documentation, prototyping, and stakeholder communication.

• Understanding of responsible AI principles, including data privacy, security, bias mitigation, explainability, human-in-the-loop workflows, and evaluation of AI-generated outputs.

• Experience with data governance, privacy, and security compliance in environments handling sensitive training, operational, or customer data.

• Strong communication and presentation skills to translate complex data, research, model behavior, and AI-enabled recommendations into actionable insights.

• Ability to work collaboratively in a multidisciplinary team and adapt to a fast-paced, evolving environment.

• A passion for learning new tools and technologies across AI, data science, XR, spatial computing, learning analytics, and immersive training.

Preferred Skills:

• 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, Business Intelligence (BI) tools, data warehouses, vector search, Large Language Model (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.

📝 Enhancement Note: The requirements emphasize a blend of experience in data-related fields (analytics, architecture, ML) and UX research, with a specific focus on learning technologies (LMS, LRS, xAPI). The "5+ years" and "3+ years" requirements suggest a mid-to-senior level candidate. The explicit mention of "generative AI tools" and "AI-assisted research workflows" points to a need for practical experience with current AI advancements. The Bachelor's degree requirement is standard, but the diverse fields listed (HCI, Cognitive Science, Learning Science) highlight the interdisciplinary nature of the role.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Showcase a minimum of 2-3 impactful projects demonstrating experience in designing or optimizing data systems for analytics, AI, or user experience enhancement.

• Include examples of analyzing complex, multimodal datasets (e.g., user behavior, telemetry, learning data) and deriving actionable insights.

• Present case studies that illustrate the application of ML, predictive analytics, or generative AI techniques to solve specific problems or improve outcomes.

• Demonstrate proficiency in data visualization and reporting, with examples of dashboards or reports that effectively communicate findings to diverse audiences.

• Highlight projects involving XR, immersive learning, or adaptive learning technologies, if applicable.

Process Documentation:

• Examples of workflow design and optimization for data pipelines, analytics processes, or research methodologies.

• Documentation of system implementation or integration efforts, particularly in data architecture or analytics platforms.

• Evidence of defining and tracking Key Performance Indicators (KPIs) and measuring the effectiveness of implemented solutions.

• Portfolios should clearly articulate the candidate's role in the process and their contribution to achieving project goals.

📝 Enhancement Note: For a role at this intersection of UX research, data, and AI, a portfolio is crucial. It should not only showcase technical skills but also the ability to translate complex data and AI insights into tangible user experience improvements and product strategy. Emphasis should be placed on demonstrating problem-solving skills, analytical thinking, and the ability to communicate complex technical concepts clearly.

💵 Compensation & Benefits

Salary Range:

Benefits:

• As this is a contract-to-hire role, benefits may be provided by the contracting agency or become available upon conversion to a full-time employee. Standard benefits often include:
  • Health, Dental, and Vision Insurance
  • Paid Time Off (PTO)
  • Potential for 401(k) matching (upon conversion to full-time)
  • Opportunities for professional development and training

Working Hours:

• Standard 40 hours per week, with potential for flexibility given the remote nature of the role.

• The role operates within U.S. time zones, likely aligning with the VP of Innovation's schedule.

📝 Enhancement Note: The hourly rate of $42-$55 for a contractor role, especially with the required 5+ years of experience and specialized skill set (UX research, AI, data architecture), appears to be on the lower end for a fully remote, contract-to-hire position in the U.S. market. Typical rates for such roles often range from $50-$80+ per hour, depending on location and specific expertise. Candidates should consider this range in the context of their experience and market value, and inquire about the conversion potential and associated full-time benefits during the interview process.

🎯 Team & Company Context

🏢 Company Culture

Industry: Technology, specifically focusing on Immersive Learning, XR (Extended Reality), AI, and Spatial Computing for training and education. HTX Labs aims to transform how the world learns.

Company Size: The provided data does not specify company size from LinkedIn. However, the presence of a VP of Innovation and a clear role structure suggests a growing company, likely beyond startup phase but not yet enterprise-level. It could range from 50-250 employees.

Founded: The company was founded in 2019. This indicates a relatively young company, likely still agile and innovative, with a strong focus on emerging technologies.

Team Structure:

• The role reports directly to the VP of Innovation, suggesting high visibility and direct impact on strategic initiatives.

• Collaboration is expected with Product, Engineering, Design, Learning, and Data teams, indicating a cross-functional and matrixed team environment.

Methodology:

• Data-Driven Decision Making: A core tenet, emphasized by the need to transform user signals into structured intelligence and foster a data-driven culture.

• Human-Centered Design: Essential for UX research, ensuring that technology solutions are designed with the end-user's needs and behaviors at the forefront.

• AI Integration: Active use of AI/ML and generative AI for research, analytics, and product enhancement.

• Agile Development: Experience with agile processes is required, suggesting iterative development cycles and adaptability.

• Continuous Learning & Experimentation: The company fosters a culture of learning, both for its users and its internal teams, with an emphasis on experimentation.

Company Website: https://htxlabs.com/

📝 Enhancement Note: HTX Labs positions itself at the cutting edge of immersive learning and AI. The company culture likely values innovation, rapid iteration, and a strong understanding of emerging technologies. For an operations professional, this means an environment that is dynamic, data-intensive, and focused on measurable impact in the rapidly evolving XR and AI sectors.

📈 Career & Growth Analysis

Operations Career Level: This role is positioned as a mid-to-senior level individual contributor, focusing on specialized technical and analytical skills within the broader GTM and product development ecosystem. It sits at the intersection of Data Science, UX Research, and AI/ML operations.

Reporting Structure: Reporting directly to the VP of Innovation provides direct access to leadership and a strategic perspective. This structure can accelerate learning and visibility for impactful contributions.

Operations Impact: The role's impact is significant, directly influencing the evolution of HTX Labs' immersive learning products (EMPACT) by transforming raw user data and AI outputs into actionable intelligence. This intelligence drives product development, enhances user experience, and optimizes learning outcomes, thereby contributing to the company's overall growth and market position.

Growth Opportunities:

• Technical Specialization: Deepen expertise in XR data analytics, AI-assisted research, and scalable data architecture for immersive environments.

• Cross-Functional Leadership: Lead data initiatives and research projects that span multiple departments, enhancing collaboration and project management skills.

• AI & XR Domain Expertise: Become a subject matter expert in the integration of AI and XR for learning solutions, a rapidly growing field.

• Contract-to-Hire Conversion: Potential to transition into a full-time role with expanded responsibilities and benefits, offering long-term career stability.

• Innovation Impact: Contribute to the development of groundbreaking learning technologies, gaining experience in a cutting-edge industry.

📝 Enhancement Note: The "Contract-to-Hire" nature of this role suggests a potential path to full-time employment, which is a significant growth opportunity. The direct reporting line to the VP of Innovation also offers a unique chance for mentorship and exposure to high-level strategic thinking within the dynamic XR and AI learning space.

🌐 Work Environment

Office Type: Fully Remote. This offers maximum flexibility for candidates across the United States.

Office Location(s): Remote (United States). This implies that candidates must be legally authorized to work in the U.S. and likely reside within a U.S. time zone for collaboration purposes.

Workspace Context:

• Flexibility and Autonomy: As a remote role, candidates will have significant autonomy over their work environment and schedule, within the constraints of collaborative team needs.

• Technology-Centric: The work environment will be heavily reliant on digital collaboration tools, cloud-based platforms, and potentially XR development environments.

• Cross-Functional Interaction: Regular interaction with diverse teams (Product, Engineering, Design, Learning, Data) will be a defining characteristic, requiring strong virtual communication and collaboration skills.

• Fast-Paced & Evolving: The company's focus on emerging technologies suggests a dynamic and potentially rapid pace of work, requiring adaptability.

Work Schedule:

• Typically 40 hours per week. While remote, adherence to standard business hours within U.S. time zones is expected for effective collaboration with the VP of Innovation and other team members. Some flexibility may be possible, but core working hours for meetings and collaboration are likely.

📝 Enhancement Note: The fully remote nature is a significant aspect of the work environment. Candidates should be self-motivated, disciplined, and possess strong virtual communication skills. The collaboration with a VP suggests a need for professionalism and strategic communication, even in a remote setting.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: Likely a brief call with HR or a recruiter to assess basic qualifications, interest, and fit for a contract-to-hire role.

• Hiring Manager Interview: A more in-depth discussion with the VP of Innovation or a direct lead to evaluate technical skills, experience, and alignment with the role's strategic objectives. Expect questions related to data architecture, UX research methodologies, AI/ML applications, and XR technologies.

• Technical/Portfolio Review: A session dedicated to reviewing the candidate's portfolio. This may involve presenting specific case studies, discussing past projects in detail, and potentially a technical assessment or coding challenge related to data analysis, SQL, or Python.

• Cross-Functional Team Interviews: Interviews with members from Product, Engineering, and Design teams to assess collaboration style, communication skills, and ability to integrate into a multidisciplinary environment.

• Final Interview/Offer: A concluding discussion, potentially with senior leadership, to finalize the assessment and extend an offer.

Portfolio Review Tips:

• Curate Strategically: Select 3-4 projects that best showcase your experience in data architecture, UX research, AI/ML application, and ideally, XR or learning analytics.

• Quantify Impact: For each project, clearly articulate the problem, your specific role and contributions, the methodologies and tools used, and most importantly, the measurable outcomes or business impact (e.g., improved user engagement by X%, reduced data processing time by Y%, informed Z product decisions).

• Highlight AI & UX Synergy: Emphasize how you've used data and AI to inform UX decisions, or how UX research has guided data/AI system design.

• Prepare for Technical Depth: Be ready to discuss your technical choices, data modeling approaches, AI/ML model selection rationale, and how you ensured data quality and responsible AI practices.

• Practice Your Narrative: Rehearse presenting your portfolio projects concisely and engagingly, anticipating questions about your process, challenges faced, and lessons learned.

Challenge Preparation:

• Data Analysis & Interpretation: Be prepared for scenarios requiring analysis of hypothetical datasets related to user behavior, learning performance, or system telemetry.

• System Design Thinking: Questions may probe your approach to designing scalable data architectures or analytics pipelines for XR applications.

• AI Application Scenarios: Be ready to discuss how you would apply AI/ML or generative AI to solve specific UX or learning challenges.

• Responsible AI Considerations: Demonstrate an understanding of ethical AI principles and how to implement them in practice.

📝 Enhancement Note: The emphasis on a portfolio review and potential technical challenges suggests that HTX Labs values demonstrable skills and practical application over theoretical knowledge. Candidates should prepare to walk through their projects in detail, explaining their thought process and quantifiable results.

🛠 Tools & Technology Stack

Primary Tools:

• Data Analysis & Visualization: Tools like Tableau, Power BI, Looker, or similar platforms for creating dashboards and reports. Proficiency in Python (with libraries like Pandas, NumPy, Scikit-learn) and SQL for data manipulation and analysis.

• AI/ML & Generative AI: Experience with AI/ML frameworks (e.g., TensorFlow, PyTorch), libraries for NLP, and familiarity with generative AI tools and LLM workflows.

• XR/Immersive Learning Data: Experience with learning analytics platforms, LRS, LMS, SCORM, xAPI, and telemetry systems specific to XR applications.

• Cloud Platforms: Familiarity with cloud data architecture concepts (e.g., AWS, Azure, GCP) and data pipeline tools.

Analytics & Reporting:

• Proficiency in analyzing user behavior, learning outcomes, and system performance data.

CRM & Automation:

• While not explicitly mentioned, experience with CRM systems (e.g., Salesforce) could be beneficial for understanding customer journeys and sales data, though less central to this specific role. Automation tools relevant to data pipelines and research workflows might be utilized.

• Integration Tools: Understanding of APIs and data integration patterns will be valuable for connecting various data sources and systems.

📝 Enhancement Note: The technology stack is heavily skewed towards data, AI, and XR. Candidates should highlight experience with Python, SQL, common data visualization tools, and any exposure to AI/ML frameworks or generative AI platforms. Familiarity with learning data standards like xAPI is a significant plus.

👥 Team Culture & Values

Operations Values:

• Innovation and Forward-Thinking: A strong emphasis on leveraging cutting-edge technologies like AI and XR to redefine learning.

• Data-Driven Decision Making: A commitment to using data and analytics to guide product development and strategy.

• Human-Centricity: A focus on understanding and improving the user experience, particularly in immersive environments.

• Collaboration and Cross-Functional Partnership: The expectation of working effectively with diverse teams to achieve shared goals.

• Continuous Learning and Improvement: A culture that encourages skill development, experimentation, and adaptation.

• Responsible Technology Adoption: A commitment to ethical AI practices, data privacy, and security.

Collaboration Style:

• Cross-Functional Integration: Expect to work closely with Product Managers, Engineers, Designers, and Learning Specialists. This requires clear communication and the ability to translate between technical and user-focused perspectives.

• Agile and Iterative: Collaboration will likely occur within agile development cycles, involving frequent feedback loops and iterative adjustments.

• Data-Informed Discussions: Discussions will be grounded in data and research findings, requiring the ability to present evidence and engage in constructive debate.

• Remote Collaboration: Proficiency with virtual collaboration tools (e.g., Slack, Zoom, Miro) and a proactive approach to communication are essential.

📝 Enhancement Note: The company culture appears to be a blend of a fast-paced tech startup and a research-oriented organization, driven by innovation and a commitment to improving learning outcomes through advanced technologies. Candidates should emphasize their adaptability, collaborative spirit, and passion for data-driven innovation.

⚡ Challenges & Growth Opportunities

Challenges:

• Navigating Evolving Technologies: Staying current with the rapid advancements in AI, generative AI, and XR requires continuous learning and adaptation.

• Data Complexity and Scale: Managing and analyzing large, multimodal datasets from diverse sources (XR telemetry, user interactions, learning systems) presents significant technical and analytical challenges.

• Translating Insights into Action: Effectively bridging the gap between complex data/AI insights and actionable product improvements or UX enhancements can be difficult.

• Responsible AI Implementation: Ensuring ethical AI practices, data privacy, and bias mitigation in a cutting-edge application area requires careful consideration and proactive measures.

• Remote Collaboration Dynamics: Maintaining strong team cohesion and effective communication in a fully remote setting requires deliberate effort and strong interpersonal skills.

Learning & Development Opportunities:

• Specialization in Emerging Fields: Gain deep expertise in XR analytics, AI-driven UX, and adaptive learning technologies.

• Exposure to Advanced AI: Work directly with generative AI models and AI-assisted research tools, pushing the boundaries of traditional UX research.

• Cross-Disciplinary Skill Development: Develop a richer understanding of product development, engineering, and learning science through close collaboration.

• Potential for Full-Time Role: A clear pathway to a permanent position with increased responsibilities, benefits, and long-term career growth within HTX Labs.

• Industry Influence: Contribute to the development of next-generation learning solutions, potentially shaping the future of immersive education and training.

📝 Enhancement Note: The challenges are directly tied to the innovative nature of the role and the company's focus on cutting-edge technologies. Candidates should view these challenges as opportunities for growth and skill development, demonstrating resilience and a proactive learning mindset.

💡 Interview Preparation

Strategy Questions:

• "Describe a complex dataset you've worked with and how you approached its analysis and interpretation. What tools and techniques did you employ?" (Focus on data architecture, analysis, and AI/ML application).

• "How would you design a data architecture to capture user behavior and learning outcomes in an XR training simulation? What key metrics would you prioritize?" (Probe data systems thinking and understanding of relevant metrics).

• "Walk me through a project where you used AI or machine learning to inform UX design or improve a product feature. What were the outcomes?" (Assess practical AI application and impact on UX/product).

• "How do you approach ensuring responsible AI practices, including data privacy, bias mitigation, and explainability, in your work?" (Evaluate understanding of ethical AI principles).

Company & Culture Questions:

• "What interests you about HTX Labs and our mission to transform learning with XR and AI?" (Assess alignment with company vision).

• "How do you stay current with trends in UX research, AI, and XR technology?" (Gauge commitment to continuous learning).

• "Describe your experience working in a remote, cross-functional team. What strategies do you use to ensure effective collaboration?" (Evaluate remote work capabilities and teamwork).

Portfolio Presentation Strategy:

• Structure: For each project, follow a clear narrative: Problem -> Your Role/Approach -> Tools/Technologies -> Key Findings/Insights -> Impact/Results.

• Quantify Everything: Use numbers and metrics to demonstrate the impact of your work.

• Highlight AI & UX Synergy: Explicitly connect how data analysis and AI informed UX decisions, or how UX research guided data strategy.

• Technical Depth: Be prepared to dive deep into your technical choices, data modeling, and AI methodologies.

• Conciseness: Practice delivering your presentations within a set timeframe, focusing on the most impactful aspects.

• Interactive Elements: If possible, include interactive dashboards or visualizations within your portfolio for a more engaging presentation.

📝 Enhancement Note: Interviews will likely assess a candidate's ability to connect technical expertise in data and AI with user-centric design principles and business objectives. Demonstrating a proactive approach to learning and an understanding of ethical considerations will be key.

📌 Application Steps

To apply for this C2H UX Data Researcher position:

• Submit your application through the provided link on the rippling.com ATS portal.

• Tailor Your Resume: Highlight experience in data architecture, UX research, AI/ML, XR, and learning analytics, using keywords from the job description. Quantify achievements whenever possible.

• Prepare Your Portfolio: Curate 3-4 strong projects that showcase your skills in data analysis, system design, AI application, and UX impact. Ensure clear documentation of your process and results.

• Research HTX Labs: Understand their mission, products (EMPACT), and the intersection of XR, AI, and learning. Familiarize yourself with their company culture and values.

• Practice Your Pitch: Be ready to articulate your experience, your approach to solving complex data and UX challenges, and your understanding of responsible AI, particularly within the context of immersive learning. Prepare to present your portfolio effectively.

• Anticipate Technical Questions: Review concepts related to data modeling, SQL, Python for data analysis, ML algorithms, and xAPI data structures.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and operations industry-standard assumptions. All details should be verified directly with the hiring organization before making application decisions.