AI First Design Researcher at PALO IT

📍 Job Overview

Job Title: AI First Design Researcher (Research Ops & Insights Enablement)

Company: PALO IT

Location: Mexico City, DF, Mexico

Job Type: Full-Time

Category: Research Operations / Insights Enablement

Date Posted: 2026-06-22

Experience Level: 5-10 Years

Remote Status: On-site

🚀 Role Summary

Establish, scale, and govern the Research Operations (Research Ops) practice to ensure continuous, reliable, and decision-oriented research across PALO IT.
Build the operational backbone for mixed-method research, enabling speed, rigor, and trust while integrating AI for accelerated analysis and synthesis.
Define and implement ethical standards, consent practices, research governance, and participant data management protocols.
Design and optimize research workflows from intake and prioritization to repository, synthesis, and activation for improved insight distribution.
Manage the research tooling ecosystem, including platforms like Dovetail, Maze, Looker, and Hotjar, and leverage AI for knowledge management and insight scaling.

📝 Enhancement Note: The raw job title is "AI First Design Researcher". However, the detailed description focuses heavily on "Research Ops & Insights Enablement," including establishing operational models, governance, workflows, and tooling for research. The "AI First" aspect is integrated into the "Your Role" and "AI-Native Engineering" sections, emphasizing AI's role in analysis, synthesis, and efficiency. Therefore, the job title has been refined to better reflect the core operational responsibilities while retaining the AI-centric context. The role category is defined as Research Operations / Insights Enablement to accurately represent the function.

📈 Primary Responsibilities

Define and scale the comprehensive Research Ops model, encompassing governance frameworks, operational standards, efficient workflows, and clear ownership structures.
Establish and enforce robust ethical standards, informed consent practices, research governance policies, and secure participant data management protocols to ensure compliance and trust.
Build and maintain operational systems designed for continuous discovery and the execution of ongoing research programs, fostering a culture of proactive learning.
Enable sophisticated mixed-method research by facilitating the structured triangulation of qualitative and quantitative signals to provide holistic insights.
Design, optimize, and streamline research workflows, covering the entire lifecycle from initial intake and prioritization to repository management, synthesis, and actionable insight activation.
Build and maintain the research tooling ecosystem, ensuring seamless integration and effective utilization of platforms such as Dovetail, Maze, Looker, and Hotjar.
Integrate AI-assisted analysis and synthesis capabilities, leveraging technologies like GPT and automation to accelerate pattern detection, summarization, and the efficient distribution of key insights.
Create scalable systems for knowledge management, including taxonomy development, effective tagging strategies, repository quality assurance, and efficient evidence retrieval.
Define, track, and report on OKRs and KPIs that are directly tied to uncertainty reduction, decision quality, research adoption rates, and overall operational efficiency.
Act as a strategic partner, collaborating closely with Product, Design, Data, and Engineering teams to embed research findings into roadmap planning and critical decision-making cycles.
Enhance the speed, consistency, and usability of research insights across all relevant teams, ensuring research findings are readily accessible and actionable.
Serve as a strategic enabler for advancing research maturity across the organization, guiding teams from ad hoc studies towards a robust and trusted continuous-learning system.

📝 Enhancement Note: The core responsibilities section has been expanded from the provided bullet points to include more granular operational tasks and strategic implications, such as defining governance frameworks, managing data security, enabling continuous discovery, and integrating AI for efficiency. This expansion aims to provide a more comprehensive view of the operational demands of the role.

🎓 Skills & Qualifications

Education: While not explicitly stated, a Bachelor's or Master's degree in a relevant field such as Human-Computer Interaction, Psychology, Sociology, Data Science, or a related discipline is typically expected for roles involving research operations and insights enablement.

Experience: 5-10 years of proven experience in Research Operations, UX Research Operations, Insights Operations, or Research Program Management is required. This experience should demonstrate a track record of establishing and scaling research operations functions.

Required Skills:

Proven experience in Research Ops, UX Research Operations, Insights Operations, or Research Program Management.
Strong command of advanced mixed methods, including qualitative and quantitative triangulation techniques for comprehensive data analysis.
Solid understanding of applied statistics and the ability to interpret research findings in a decision-oriented manner.
Hands-on experience with research repositories and insight platforms such as Dovetail and Maze for organized data storage and retrieval.
Familiarity with analytics and behavior tools such as Looker and Hotjar for understanding user interactions and performance metrics.
Experience using AI tools for research analysis, synthesis, and knowledge scaling to enhance efficiency and insight generation.
Strong understanding of research governance, ethics, consent, and operational quality to ensure responsible research practices.
Ability to design scalable processes that support continuous discovery and democratized research access for broader organizational benefit.
Proficiency in critical thinking to analyze complex research challenges and identify effective solutions.
Advanced synthesis and pattern recognition skills to distill large datasets into actionable insights.
Excellent stakeholder management abilities to effectively communicate research plans, findings, and operational needs.
Strong analytical judgment to make sound decisions based on research data and operational metrics.
Ability to bring clarity and structure to complex and ambiguous research environments.
Experience with AI-Native Engineering practices, including prompt engineering and validation of Generative AI output. Preferred Skills:
Experience with AI tools for research analysis, synthesis, and knowledge scaling, particularly leveraging GPT and automation.
Experience in defining and tracking OKRs and KPIs specifically for research operations and insights enablement.
Familiarity with behavioral analytics tools beyond Hotjar, if applicable.
Experience in implementing AI-driven development practices or supporting such initiatives.

📝 Enhancement Note: The required skills section has been augmented to include specific AI-related proficiencies mentioned in the "AI-Native Engineering" section of the original description, such as prompt engineering and validation of GenAI output, as these are critical for the "AI First" aspect of the role. Preferred skills have been added to highlight areas that would further strengthen a candidate's profile.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

Demonstrable experience in designing and implementing robust Research Operations frameworks, showcasing scalable processes and governance structures.
Case studies detailing successful establishment or optimization of research workflows, from intake to insight activation, emphasizing efficiency gains and improved research quality.
Examples of building and managing research repositories or knowledge management systems, highlighting taxonomy, tagging, and retrieval strategies.
Evidence of integrating AI tools or automation into research analysis, synthesis, or operational processes, with quantifiable outcomes or efficiency improvements.
Documentation of research governance, ethical protocols, and consent management systems implemented in previous roles. Process Documentation:
Detailed documentation of research intake and prioritization processes, including stakeholder engagement and roadmap alignment.
Workflows illustrating the end-to-end research lifecycle, from participant recruitment and study execution to data analysis and insight dissemination.
Process maps for AI-assisted analysis and synthesis, demonstrating how AI tools are leveraged to accelerate research outcomes.
Protocols for managing research data, ensuring privacy, security, and compliance with ethical guidelines.
Frameworks for knowledge management and insight sharing, including taxonomy, tagging, and repository maintenance.

📝 Enhancement Note: This section has been structured to reflect the operational nature of the role, emphasizing the need for candidates to showcase their ability to design, implement, and document processes. The focus is on demonstrating practical experience in building and managing research operations, with a specific emphasis on AI integration and governance.

💵 Compensation & Benefits

Salary Range: Based on the experience level (5-10 years), location (Mexico City), and the specialized nature of Research Operations and AI integration, a competitive salary range for this role in Mexico City would likely fall between MXN $60,000 to MXN $90,000 per month, or approximately USD $35,000 to $53,000 annually. This estimate is derived from industry benchmarks for senior-level research operations and data science roles in major metropolitan areas, considering the cost of living and the demand for specialized tech skills in Mexico.

Benefits:

Stimulating working environments that foster innovation and collaboration.
Unique career path with opportunities for professional development and advancement.
International mobility, allowing for global collaboration and potential assignments.
Involvement in internal R&D projects, contributing to cutting-edge technology and methodologies.
Robust knowledge-sharing culture, with opportunities to learn from and contribute to a global network of experts.
Personalized training programs to support continuous skill development.
Opportunities for entrepreneurship and intrapreneurship, encouraging innovation and initiative.

Working Hours: 40 hours per week, with potential for flexibility to accommodate research project needs and global team collaboration, aligning with typical full-time employment standards.

📝 Enhancement Note: A salary range has been estimated as it was not provided in the raw data. The estimation methodology considers the role's seniority, specialized skills (Research Ops, AI), and the specific geographic location (Mexico City), referencing general market data for comparable positions. The listed benefits are directly extracted from the "What We Offer" section of the provided description.

🎯 Team & Company Context

🏢 Company Culture

Industry: Technology Consultancy, specializing in digital and sustainable products and services. PALO IT positions itself as a "force for good," focusing on the triple bottom line: people, planet, profit. The company is recognized as a World Economic Forum New Champion and is B Corp-certified, indicating a strong commitment to social and environmental responsibility.

Company Size: +650 experts across 18 offices on 5 continents, with employees from over 50 nationalities. This indicates a globally distributed, mid-to-large-sized organization with significant reach and diverse talent.

Founded: PALO IT is a robust and resilient company, noted as being 100% independent and having 0 debt, suggesting a stable and well-established business.

Team Structure:

The Research Ops team is likely to be part of a larger Product, Design, or Data organization, working closely with cross-functional teams.
Reporting structure would likely involve reporting to a Head of Research, Director of Product, or a similar senior leadership role overseeing insights and product development.
Cross-functional collaboration is a key expectation, with significant interaction with Product Managers, UX Designers, Data Scientists, and Engineers to embed research into the product development lifecycle. Methodology:
Data analysis and insights methods will be central, with an emphasis on leveraging both traditional research techniques and advanced AI capabilities for pattern detection and synthesis.
Workflow planning and optimization strategies will be critical for establishing and scaling the Research Ops practice, ensuring efficiency and reliability.
Automation and efficiency practices are expected, particularly through the integration of AI tools in research analysis and operational processes.

Company Website: https://www.palo-it.com/

📝 Enhancement Note: Company context has been synthesized from the provided "Who We Are" and "More About PALO IT" sections, focusing on aspects relevant to an operations role, such as company size, global reach, and commitment to sustainability and ethical practices (B Corp). The "AI First" and "Gen-e2" initiatives are highlighted as core to their operational and development methodology.

📈 Career & Growth Analysis

Operations Career Level: This role is positioned as a senior-level individual contributor or a foundational lead role within Research Operations. It requires significant experience to establish and scale a practice, manage complex operations, and integrate advanced technologies like AI. The scope involves defining operational models, governance, and systems, indicating a strategic impact beyond day-to-day task execution.

Reporting Structure: The role will likely report to a senior leader within Product, Design, or Data, such as a Head of Research, Director of Product Insights, or VP of Product. The position requires close collaboration with various departments, including Product, Design, Data, and Engineering.

Operations Impact: The Research Ops & Insights Enablement role has a direct impact on the organization's ability to make data-driven decisions. By establishing reliable research processes, ensuring high-quality insights, and accelerating the research cycle through AI, this role enables faster product development, reduced uncertainty, and improved product-market fit, ultimately contributing to business success and innovation.

Growth Opportunities:

Operations Skill Advancement: Opportunity to become a subject matter expert in Research Operations and AI-driven research methodologies, potentially leading to a Research Ops Manager or Director role.
Leadership Development: Potential to build and lead a Research Ops team as the practice scales within PALO IT.
Specialization: Deepen expertise in AI integration for research, prompt engineering, and advanced data analysis within a cutting-edge "AI First" development environment.
Cross-functional Expertise: Gain comprehensive understanding of product development lifecycles and strategic decision-making processes across different departments.

📝 Enhancement Note: The career and growth analysis is based on the seniority implied by the responsibilities (establishing and scaling a practice) and the specialized nature of the role. It outlines potential career trajectories within the operations and research domain, emphasizing continuous learning and leadership potential.

🌐 Work Environment

Office Type: The role is specified as "On-site" in Mexico City. This suggests a traditional office environment designed for in-person collaboration, team meetings, and focused work. PALO IT's global presence with multiple offices implies a professional, modern workspace.

Office Location(s): Mexico City, DF, Mexico. Specific office details are not provided, but candidates should anticipate a professional office setting within a major metropolitan area.

Workspace Context:

The workspace will likely be designed to foster collaboration, with opportunities for direct interaction with Product, Design, Data, and Engineering teams.
Access to necessary operations tools and technology, including research platforms (Dovetail, Maze), analytics tools (Looker, Hotjar), and AI development tools (GitHub Copilot), will be provided.
The environment will support a blend of focused individual work on operational systems and collaborative sessions for strategy, workflow design, and insight synthesis.

Work Schedule: The standard working hours are 40 hours per week. While the role is on-site, PALO IT's culture may offer some level of flexibility, but the core expectation is consistent presence for team collaboration and operational duties.

📝 Enhancement Note: Based on the "On-site" remote status and general understanding of tech consultancy office environments, this section details the likely physical and collaborative aspects of the workspace.

📄 Application & Portfolio Review Process

Interview Process:

Initial Screening: A review of your resume and portfolio to assess experience in Research Ops, AI integration, and relevant tooling.
Hiring Manager Interview: Discussion focused on your experience establishing and scaling research operations, understanding of mixed methods, and approach to governance and ethics.
Technical/Case Study Interview: A practical assessment, potentially involving a case study or problem-solving exercise related to optimizing research workflows, integrating AI for analysis, or managing research data. Preparation should include examples of how you've tackled similar challenges.
Cross-functional Team Interviews: Meetings with potential collaborators from Product, Design, Data, and Engineering to evaluate your stakeholder management skills, communication style, and ability to integrate research into their processes.
Final Interview: A discussion with senior leadership to assess strategic thinking, cultural fit, and long-term vision for Research Ops at PALO IT.

Portfolio Review Tips:

Showcase Operational Impact: Highlight tangible achievements in establishing, scaling, or optimizing Research Ops functions. Quantify improvements in efficiency, research quality, or decision-making speed.
Demonstrate AI Integration: Include specific examples of how you've used AI tools (e.g., GPT, Copilot) for research analysis, synthesis, or operational tasks. Detail the prompts used and the outcomes achieved.
Process Documentation: Present clear examples of research workflows, governance models, and knowledge management systems you've designed or implemented.
Case Study Structure: For any case study, clearly outline the problem, your approach (including tools and methodologies), the actions taken, and the measurable results or impact.
Tool Proficiency: Be prepared to discuss your experience with specific tools like Dovetail, Maze, Looker, and Hotjar, and how you leveraged them to achieve operational goals.

Challenge Preparation:

Research Workflow Optimization: Be ready to propose solutions for improving the efficiency and effectiveness of a given research workflow, considering both traditional and AI-driven approaches.
AI Integration Strategy: Prepare to discuss how you would integrate AI into a specific research process (e.g., thematic analysis of interview transcripts, synthesizing findings from multiple studies).
Research Governance Scenarios: Consider how you would address ethical dilemmas, consent management, or data privacy issues within a research context.
Stakeholder Alignment: Practice articulating the value of Research Ops and insights to different stakeholders (e.g., Product Managers, Engineers) and how you would foster collaboration.

📝 Enhancement Note: This section provides actionable advice for candidates, focusing on how to prepare their portfolio and interview responses to align with the specific requirements of a Research Ops role, with a strong emphasis on AI integration and operational excellence.

🛠 Tools & Technology Stack

Primary Tools:

Research Repositories & Insight Platforms: Dovetail, Maze (essential for managing research data, conducting studies, and synthesizing findings).
AI Tools for Research Analysis & Synthesis: Leveraging GPT-based models and automation tools to accelerate pattern detection, summarization, and insight generation.
AI-Native Engineering Assistants: GitHub Copilot, Cursor (for code scaffolding, generation, optimization, documentation, and testing, reflecting the "AI First" development approach).
Prompt Engineering Tools/Platforms: For designing and refining prompts to elicit reliable and high-quality output from Generative AI.

Analytics & Reporting:

Business Intelligence/Analytics: Looker (for data visualization, dashboard creation, and cross-functional tracking of research impact and operational KPIs).
Behavioral Analytics Tools: Hotjar (for understanding user behavior on digital platforms, complementing qualitative research).
Data Triangulation Tools: Any tools or methodologies used to combine qualitative and quantitative data for comprehensive analysis.

CRM & Automation:

While not explicitly mentioned for this role, understanding CRM principles for participant management or project tracking might be beneficial.
Automation tools for workflow management and data processing will be key for scaling the Research Ops practice.

📝 Enhancement Note: This section details the specific tools and technologies mentioned in the job description, categorizing them by function and highlighting their relevance to the Research Ops and "AI First" aspects of the role.

👥 Team Culture & Values

Operations Values:

AI-First Mindset: A core value is the integration of AI across all development and operational processes, viewing AI as a primary enabler of productivity and quality.
Excellence and Rigor: Commitment to delivering high-quality work, as evidenced by exceeding traditional team output with AI generation and maintaining high standards in research.
Collaboration and Collective Intelligence: Working as a unified team, sharing knowledge, and leveraging diverse expertise to achieve common goals.
Doers and Committed: A proactive, hands-on approach to problem-solving and execution, with a strong commitment to delivering results.
Positive and Courageous: Embracing challenges, driving innovation, and maintaining a positive attitude throughout the development and operational processes.
Impact-Driven: Focusing on delivering value across the triple bottom line (people, planet, profit) and continuously measuring and improving outcomes.

Collaboration Style:

Cross-functional Integration: Seamless collaboration across Product, Design, Data, and Engineering teams is essential for embedding research insights and operational best practices.
Data-Driven Decision Making: Fostering a culture where decisions are informed by robust research, data analysis, and AI-generated insights.
Continuous Learning and Improvement: An environment that encourages experimentation, learning from feedback, and adapting processes to enhance efficiency and effectiveness.
Open Communication: Encouraging open dialogue, knowledge sharing, and constructive feedback to drive innovation and operational excellence.

📝 Enhancement Note: This section synthesizes the company's stated values and culture from various parts of the job description, framing them within the context of an operations professional and highlighting the importance of the "AI First" approach.

⚡ Challenges & Growth Opportunities

Challenges:

Scaling Research Ops: Establishing and scaling a robust Research Ops practice from the ground up in a rapidly evolving organization.
AI Integration Complexity: Effectively integrating AI tools for research analysis and synthesis while ensuring data quality, ethical compliance, and actionable insights.
Cross-functional Alignment: Ensuring consistent adoption and utilization of research insights and operational processes across diverse departments with varying priorities.
Maintaining Research Rigor: Balancing the speed and efficiency gained from AI with the necessary rigor and validity of mixed-method research.
Evolving AI Landscape: Staying current with rapid advancements in AI technology and adapting operational strategies accordingly.

Learning & Development Opportunities:

AI & Prompt Engineering Mastery: Deepen expertise in leveraging Generative AI for research and development, including advanced prompt engineering techniques.
ResearchOps Leadership: Develop skills in building and managing research operations teams and frameworks within a global consultancy.
Sustainable Technology Practices: Contribute to and learn about PALO IT's commitment to sustainability and delivering tech as a force for good.
Advanced Analytics & Data Triangulation: Enhance skills in combining qualitative and quantitative data, augmented by AI, for richer insights.
Industry Best Practices: Gain exposure to cutting-edge methodologies in an "AI First" development environment and a B Corp-certified company.

📝 Enhancement Note: This section outlines potential challenges specific to the role and the company's innovative approach, alongside clear growth opportunities that align with the evolving landscape of research and AI.

💡 Interview Preparation

Strategy Questions:

How would you design and implement a Research Ops model from scratch, including governance, standards, and workflows, for an "AI First" organization?
Describe your experience integrating AI tools for research analysis and synthesis. What specific tools have you used, and what were the key outcomes or challenges?
How do you ensure ethical research practices, including consent and data privacy, when operating at scale and leveraging AI?
What are your strategies for triaging research requests and prioritizing them based on business impact and uncertainty reduction?
How would you measure the success of a Research Ops function and demonstrate its ROI to stakeholders? Company & Culture Questions:
What attracts you to PALO IT's mission of "tech as a force for good" and its "AI First" approach?
How do you align with PALO IT's values of being positive, courageous, caring, and committed to excellence?
How would you foster collaboration between Research Ops and cross-functional teams like Product, Design, Data, and Engineering?
Describe a time you had to bring clarity to a complex or ambiguous situation within a research or operational context. Portfolio Presentation Strategy:
Quantify Impact: For each project, clearly state the problem, your role, the actions taken (especially AI integration), and the measurable outcomes (e.g., % increase in efficiency, % reduction in uncertainty, speed improvements).
Showcase Process Design: Present visual aids (flowcharts, diagrams) of research workflows, governance structures, or knowledge management systems you've developed.
AI Integration Examples: Walk through a specific example of using AI for research analysis or synthesis, detailing the prompts, tools used, and the insights generated.
Tailor to PALO IT: Connect your experience and portfolio examples to PALO IT's specific initiatives like Gen-e2 and their "AI First" development philosophy.
Be Ready for Q&A: Anticipate questions about your approach to challenges, your decision-making process, and how you handle trade-offs between speed, rigor, and cost.

📝 Enhancement Note: This section provides targeted interview preparation advice, including potential strategy and behavioral questions, as well as specific guidance on how to present a portfolio effectively for this unique AI-focused Research Ops role.

📌 Application Steps

To apply for this operations position:

Submit your application through the Greenhouse application link provided.
Customize Your Resume: Ensure your resume clearly highlights experience in Research Operations, Insights Enablement, mixed-methods research, AI tool usage (especially for analysis/synthesis), and any experience with research governance or ethical protocols. Use keywords from the job description.
Prepare Your Portfolio: Curate a portfolio that showcases your ability to design, implement, and scale operational processes. Include case studies demonstrating AI integration, workflow optimization, and quantifiable impact. Be ready to present and discuss these examples.
Research PALO IT: Understand their "AI First" philosophy, Gen-e2 initiative, B Corp certification, and commitment to sustainability. Prepare to discuss how your skills and experience align with their mission and values.
Practice Interview Responses: Rehearse answers to common Research Ops and AI integration questions, focusing on providing specific examples and demonstrating strategic thinking and operational excellence.

⚠️ 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.

AI First Design Researcher

📍 Job Overview

🚀 Role Summary

📈 Primary Responsibilities

🎓 Skills & Qualifications

📊 Process & Systems Portfolio Requirements

💵 Compensation & Benefits

🎯 Team & Company Context

🏢 Company Culture

📈 Career & Growth Analysis

🌐 Work Environment

📄 Application & Portfolio Review Process

🛠 Tools & Technology Stack

👥 Team Culture & Values

⚡ Challenges & Growth Opportunities

💡 Interview Preparation

📌 Application Steps

Application Requirements

AI First Product Designer

User Researcher

UX Researcher

Design Director, Experience Design

Lead User Researcher

Architectural Design Manager | Multifamily

Senior UX Designer (f/m/d) AI Agent Conversational Experience

Senior UX Designer (f/m/d) AI Agent Conversational Experience

2026 Startup Lab Associate – UX Design and Research [HYD]

Graphic Designer (all genders)

Platform

Jobs

Links