UX Survey Scientist - New York

📍 Job Overview

Job Title: UX Survey Scientist

Company: Photon

Location: New York, New York, United States

Job Type: Full-time

Category: Quantitative Research Scientist / Survey Methodology

Date Posted: September 08, 2026

Experience Level: 10+ years

Remote Status: On-site

🚀 Role Summary

• Lead the design, validation, and implementation of quantitative research instruments and survey methodologies to support an AI-powered research intelligence system.

• Develop and maintain a robust library of validated scales and reusable frameworks for core constructs, ensuring consistent and reliable measurement across diverse research studies.

• Conduct advanced statistical analyses, including segmentation, inferential statistics, and preference modeling, to derive actionable insights from complex quantitative datasets.

• Partner closely with qualitative researchers and behavioral analysts to integrate quantitative findings into a unified, AI-augmented insights repository.

• Define and enforce rigorous confidence standards for research findings, ensuring statistical significance and directional insights are clearly distinguished and consistently applied.

📝 Enhancement Note: This role is positioned as a senior-level quantitative research scientist, focusing heavily on the methodological rigor and scalability of survey research within a larger research intelligence initiative. The emphasis on an "insights repository" and "AI-powered research" suggests a strategic role in building foundational data infrastructure for advanced analytics and machine learning applications.

📈 Primary Responsibilities

• Design and statistically validate quantitative research instruments (surveys, questionnaires) to ensure reliability, minimize bias, and accurately measure intended constructs.

• Develop and manage a centralized library of validated scales and reusable measurement frameworks to standardize data collection and enable longitudinal analysis.

• Lead the application of advanced segmentation techniques, including latent class analysis, factor analysis, cluster analysis, and Principal Component Analysis (PCA), to identify distinct audience segments.

• Perform comprehensive inferential statistical analyses, including significance testing, regression modeling, ANOVA, and advanced preference measurement techniques such as MaxDiff and conjoint analysis.

• Establish and enforce clear confidence thresholds for statistical significance and directional findings within the insights repository, ensuring consistent interpretation and application.

• Collaborate with qualitative research teams to ensure quantitative study designs are informed by exploratory insights and that findings can be seamlessly integrated into a unified research intelligence platform.

• Translate complex statistical results and methodologies into clear, concise, and actionable insights for non-technical stakeholders, managing expectations regarding certainty and statistical inference.

• Contribute to the development and maintenance of sampling designs, including stratified and quota-based approaches, to ensure representative data collection from target populations.

📝 Enhancement Note: The responsibilities highlight a hands-on approach to quantitative research, requiring deep technical expertise in survey design, statistical analysis, and segmentation. The integration aspect with qualitative research and AI suggests a forward-thinking approach to research operations.

🎓 Skills & Qualifications

Education: Master's degree or PhD in Psychology, Statistics, Cognitive Science, Sociology, or a related quantitative field.

Experience: 8+ years of applied quantitative research experience, with demonstrated ownership from study design through analysis and insight delivery. Experience with longitudinal research programs and mixed-methods environments is highly valued. Experience with B2B, professional, or institutional audiences, particularly in financial services, is preferred.

Required Skills:

• Expertise in survey design, questionnaire development, and validation techniques.

• Proficiency in confirmatory factor analysis (CFA) and item response theory (IRT) for scale development and validation.

• Applied experience in sampling design, including stratified and quota-based approaches.

• Hands-on experience with segmentation methodologies: latent class analysis, factor analysis, cluster analysis, and Principal Component Analysis (PCA).

• Strong command of inferential statistics: regression modeling, ANOVA, and correction for multiple comparisons.

• Experience with advanced preference measurement: MaxDiff and/or conjoint analysis.

• Production-level proficiency in R or Python for statistical analysis.

• Familiarity with survey platforms such as Qualtrics or equivalent.

• Proven ability to communicate complex statistical findings clearly to non-technical stakeholders. Preferred Skills:

• Experience designing longitudinal or multi-wave research programs for trend analysis.

• Demonstrated experience working in a mixed-methods environment, collaborating with qualitative researchers.

• Experience with B2B, professional, or institutional audiences.

• Financial services context is an advantage.

📝 Enhancement Note: The requirements emphasize a strong academic foundation combined with extensive practical experience. The technical skill set is highly specific, indicating a need for a seasoned professional who can immediately contribute to complex analytical challenges and build foundational research infrastructure.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Showcase a portfolio of completed quantitative research projects, demonstrating end-to-end ownership from problem definition to actionable insights.

• Include detailed case studies of survey instrument design and validation, highlighting methodological rigor and statistical soundness.

• Present examples of segmentation framework development (e.g., latent class, cluster analysis), clearly outlining the methodology and the resulting actionable segments.

• Demonstrate experience with advanced statistical analyses (e.g., regression, conjoint, MaxDiff), including the rationale for method selection and interpretation of results.

• Provide evidence of translating complex statistical findings into clear, business-relevant recommendations for non-technical audiences. Process Documentation:

• Document the process for developing and validating new survey scales and reusable measurement frameworks, including psychometric testing and reliability checks.

• Outline the methodology for defining and enforcing confidence standards for research findings within an insights repository.

• Illustrate the process of integrating quantitative research findings with qualitative insights and behavioral data for a holistic understanding.

• Detail the workflow for conducting advanced preference measurement studies, from experimental design to data analysis and reporting.

📝 Enhancement Note: For a role of this seniority and technical depth, a strong portfolio is crucial. It should not just list projects but critically detail the process and methodology used, emphasizing the scientific rigor and impact of the work. The focus on an "insights repository" implies a need to show how individual projects contribute to a larger, systematized knowledge base.

💵 Compensation & Benefits

Salary Range: USD 42,000 - USD 148,000 per year.

Benefits:

• Medical insurance

• Vision insurance

• Dental insurance

• 401k retirement plan

• Variable pay/incentives

• Paid time off (PTO)

• Paid holidays

Working Hours: Approximately 40 hours per week, typical for a full-time, on-site role.

📝 Enhancement Note: The salary range provided is broad, reflecting compensation based on actual candidate experience and qualifications. The New York City location suggests that compensation at the higher end of this range would be competitive for a senior professional with 8+ years of specialized experience. The benefits package is comprehensive, typical for full-time employees in a corporate setting.

🎯 Team & Company Context

🏢 Company Culture

Industry: Technology / Financial Services (implied by the parent company, Citi, and the role's focus)

Company Size: Large Enterprise (implied by the association with Citi)

Founded: Photon appears to be an internal initiative or division within Citi, which was founded in 1812. This suggests a long-standing, established corporate environment with a focus on innovation within financial services.

Team Structure:

• The role is part of a quantitative research workstream focused on building an "insights repository" and an "AI-powered research intelligence system."

• This team likely includes other quantitative researchers, qualitative researchers, behavioral analysts, and potentially data engineers or AI specialists.

• Reporting structure is implied to be within a research or analytics division, with potential collaboration across product, marketing, and strategy teams. Methodology:

• Data-driven decision-making and rigorous scientific methodology are core to this role.

• Emphasis on quantitative rigor, statistical validation, and the development of reusable, scalable research frameworks.

• Integration of quantitative findings with qualitative insights and behavioral data for a holistic, AI-augmented approach to research.

Company Website: https://www.photon.com (Note: This URL was inferred from the "domain_derived" field. The provided "organization_url" points to an Oracle Cloud HCM system, which is a recruitment platform, not the company's main website.)

📝 Enhancement Note: The role is embedded within a larger initiative at Citi, implying a significant impact and access to substantial resources. The culture likely blends corporate structure with a drive for innovation in data science and AI, demanding both methodological excellence and adaptability.

📈 Career & Growth Analysis

Operations Career Level: Senior Quantitative Research Scientist. This level indicates a high degree of autonomy, deep specialization, and the expectation to lead significant analytical workstreams. The role is pivotal in establishing methodological best practices and foundational data structures.

Reporting Structure: The Survey Research Scientist will likely report to a Director or VP of Research, Analytics, or Data Science. They will collaborate closely with peers in qualitative research and behavioral analysis, as well as with stakeholders in product, marketing, and strategy.

Operations Impact: This role has a direct impact on the strategic direction and operational efficiency of research initiatives. By building a reliable insights repository and ensuring rigorous measurement, the scientist enables more informed decision-making, better product development, and more effective customer understanding, ultimately driving business outcomes and competitive advantage through data.

Growth Opportunities:

• Specialization: Deepen expertise in advanced statistical techniques, AI/ML integration with research data, or specific domains like customer segmentation or preference modeling.

• Leadership: Transition into leading a team of quantitative researchers, managing research projects, or defining the strategic direction of the insights repository.

• Cross-functional Advancement: Move into roles focused on data strategy, product analytics, or advanced analytics leadership within the broader organization.

• Methodological Innovation: Drive the adoption of cutting-edge research methodologies and technologies within the organization.

📝 Enhancement Note: This is a critical role for career growth in quantitative research. The focus on building foundational systems and integrating with AI suggests opportunities to evolve into leadership positions that shape the future of research intelligence within a large financial institution.

🌐 Work Environment

Office Type: On-site in New York City. This implies a traditional office setting within a corporate environment.

Office Location(s): 115 W 30th ST STE 910, New York, NY 10001-4000. This location is in Manhattan, suggesting a professional office building environment conducive to collaboration.

Workspace Context:

• The workspace is expected to be collaborative, with opportunities to interact with fellow researchers, analysts, and stakeholders.

• Access to advanced analytical tools, software (R, Python, Qualtrics), and potentially high-performance computing resources will be available.

• The environment will likely foster a blend of focused individual work (analysis, coding) and team-based problem-solving sessions.

Work Schedule: Standard full-time hours (approx. 40 hours/week) are expected, with potential for flexibility depending on project deadlines and team norms within the corporate structure.

📝 Enhancement Note: Being an on-site role in New York City indicates a professional, potentially fast-paced corporate environment. The emphasis on collaboration suggests an office culture that values in-person interaction for brainstorming and problem-solving.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: HR or recruiter review of application, focusing on alignment with core qualifications and experience.

• Technical Interview(s): In-depth discussions with hiring manager and/or senior team members to assess technical proficiency in survey methodology, statistical analysis (R/Python, CFA, IRT, segmentation, conjoint/MaxDiff), and previous project experience. Expect coding challenges or scenario-based questions.

• Portfolio Review: Presentation of key projects from the candidate's portfolio, focusing on methodology, challenges, solutions, and impact. This is where candidates demonstrate their ability to translate complex data into actionable insights.

• Behavioral/Cultural Fit Interview: Assessment of collaboration skills, communication style, problem-solving approach, and alignment with company values, particularly regarding data integrity and scientific rigor.

• Final Interview: Potentially with a more senior leader to discuss strategic vision and overall fit for the initiative.

Portfolio Review Tips:

• Structure: Organize your portfolio logically, perhaps by methodology or project type. For each project, clearly state the problem, your role, the methods used (with specific statistical techniques), key findings, and the business impact or recommendations.

• Methodology Focus: Emphasize your expertise in survey design, validation, segmentation, and advanced statistical techniques. Be prepared to defend your methodological choices.

• Data Visualization: Include clear, well-designed charts and graphs to illustrate findings. For segmentation, show cluster profiles. For preference modeling, present utility scores or preference shares.

• Communication: Practice explaining complex statistical concepts and findings in a way that is accessible to non-technical audiences. Focus on the "so what?" – the business implications of your research.

• Quantitative Rigor: Highlight how you ensured statistical validity, addressed potential biases, and defined confidence standards for your findings.

Challenge Preparation:

• Statistical Scenarios: Be ready to discuss how you would approach common quantitative research problems, such as designing a survey for a new product concept, segmenting a customer base, or analyzing preference data.

• Coding Proficiency: Practice writing R or Python code for common statistical tasks, including data cleaning, descriptive statistics, regression, factor analysis, and potentially latent class analysis or conjoint analysis simulation.

• Methodological Debates: Be prepared to discuss the pros and cons of different methodologies (e.g., MaxDiff vs. conjoint, different segmentation approaches) and justify your preferred methods.

📝 Enhancement Note: The interview process will heavily scrutinize technical expertise and practical application. A strong portfolio that showcases rigorous methodology, clear communication, and demonstrable impact is paramount. Candidates should be prepared to discuss their work in detail and defend their analytical choices.

🛠 Tools & Technology Stack

Primary Tools:

• Statistical Software: R (advanced proficiency), Python (production-level proficiency).

• Survey Platforms: Qualtrics (familiarity), or equivalent enterprise-grade survey software.

• Data Analysis & Visualization: Tools within R/Python (e.g., ggplot2, tidyverse), potentially Tableau or Power BI for dashboarding and reporting.

Analytics & Reporting:

• Statistical Packages: Libraries within R and Python for advanced statistical modeling, machine learning, and data manipulation.

• Reporting Tools: Ability to create clear, compelling reports and presentations summarizing complex quantitative findings.

CRM & Automation:

• While not a direct CRM role, understanding how survey data integrates with CRM systems (e.g., Salesforce) for audience segmentation and campaign analysis would be beneficial.

• Familiarity with data integration processes to feed insights into an "AI-augmented repository."

📝 Enhancement Note: The technology stack is heavily focused on programming languages for statistical analysis and enterprise survey platforms. Proficiency in R and Python is non-negotiable, and experience with Qualtrics is highly desirable. The ability to integrate data into larger systems is also a key implied requirement.

👥 Team Culture & Values

Operations Values:

• Integrity & Rigor: Upholding the highest standards of scientific integrity, ensuring all research is methodologically sound and data is analyzed accurately.

• Data-Driven Insights: Committing to evidence-based decision-making, translating complex data into clear, actionable insights that drive business strategy.

• Innovation: Continuously exploring and implementing new research methodologies, statistical techniques, and technologies to enhance research capabilities.

• Collaboration: Fostering a collaborative environment where diverse perspectives (quantitative, qualitative, behavioral) are valued and integrated to achieve a holistic understanding.

• Efficiency & Scalability: Developing reusable frameworks and processes to ensure research can be conducted efficiently and scaled across the organization.

Collaboration Style:

• Cross-functional: Working closely with qualitative researchers, behavioral analysts, data scientists, and potentially product managers and marketing teams.

• Consultative: Acting as a subject matter expert, advising on research design, methodology, and interpretation of quantitative data.

• Feedback-Oriented: Open to constructive feedback on methodologies and findings, and providing clear, actionable feedback to collaborators.

• Knowledge Sharing: Actively participating in sharing best practices, methodologies, and insights within the team and broader organization.

📝 Enhancement Note: The culture values scientific precision, collaborative problem-solving, and a forward-thinking approach to research powered by AI and data. Candidates should demonstrate a commitment to methodological excellence and an ability to work effectively in a multidisciplinary team.

⚡ Challenges & Growth Opportunities

Challenges:

• Scalability of Rigor: Ensuring that advanced survey methodologies and statistical rigor can be consistently applied across a growing volume of research within the insights repository.

• Integration Complexity: Seamlessly integrating quantitative findings with qualitative insights and behavioral data into a unified, AI-augmented system.

• Communicating Uncertainty: Effectively communicating the nuances of statistical significance versus directional findings to stakeholders who may not have a deep statistical background.

• Methodological Evolution: Keeping pace with rapid advancements in survey methodology, AI, and statistical techniques to maintain a cutting-edge research program.

Learning & Development Opportunities:

• Advanced Methodologies: Opportunities to explore and implement cutting-edge statistical techniques, AI/ML applications in research, and new survey technologies.

• Domain Expertise: Deepen understanding of financial services audiences, B2B markets, and their unique research needs.

• Leadership Development: Potential to lead research initiatives, mentor junior team members, and contribute to strategic research planning.

• Industry Exposure: Participation in relevant conferences, workshops, and professional development programs focused on survey research, data science, and AI.

📝 Enhancement Note: This role presents significant challenges related to scaling complex methodologies and integrating diverse data sources. However, these challenges are directly linked to substantial growth opportunities in specialized quantitative research, AI integration, and leadership within a prominent financial institution.

💡 Interview Preparation

Strategy Questions:

• "Describe a time you designed a survey instrument from scratch. What were the key considerations for ensuring its validity and reliability?" (Focus on construct definition, question wording, scale selection, pre-testing, and statistical validation).

• "How would you approach segmenting the customer base of a financial services product using quantitative data? Which techniques would you consider and why?" (Discuss latent class, cluster analysis, factor analysis, and criteria for segment selection).

• "Imagine you have a dataset with mixed quantitative and qualitative findings. How would you integrate these to form a cohesive narrative for senior leadership?" (Emphasize triangulation, identifying convergent and divergent findings, and framing insights for strategic impact). Company & Culture Questions:

• "What are your thoughts on the ethical considerations of using AI in research and data analysis?" (Demonstrate awareness of bias, privacy, and responsible AI practices).

• "How do you ensure that your statistical findings are communicated clearly and accurately to non-technical stakeholders, managing expectations about certainty?" (Provide examples of simplification, visualization, and framing of results).

• "Describe your experience working in a mixed-methods research environment. What are the benefits and challenges of integrating quantitative and qualitative approaches?" (Highlight collaboration, complementary insights, and potential integration hurdles). Portfolio Presentation Strategy:

• Narrative Arc: For each project, tell a story: the problem statement, your approach (methodology), the execution (tools, data), the key findings, and the impact or recommendation.

• Methodological Depth: Be prepared to deep-dive into the statistical techniques used. Explain why you chose a particular method (e.g., why IRT for scale development, why MaxDiff for feature preference).

• Visual Clarity: Use clear, professional slides with well-designed charts and graphs. For segmentation, show clear profiles. For preference data, show utility scores or preference shares.

• Impact Focus: Quantify the impact of your work whenever possible (e.g., "led to a 15% improvement in customer retention," "identified a new segment representing $X million in potential revenue").

• Conciseness: Practice delivering your presentation within a set timeframe, focusing on the most critical aspects of each project.

📝 Enhancement Note: Interview preparation should focus on demonstrating deep technical expertise, strategic thinking about research design and application, and excellent communication skills. Candidates must be ready to articulate their process and defend their methodological choices with confidence.

📌 Application Steps

To apply for this operations position:

• Submit your application through the provided Oracle Cloud HCM link.

• Portfolio Customization: Tailor your resume and cover letter to highlight your most relevant quantitative research, survey methodology, and statistical analysis experience, specifically referencing skills like CFA, IRT, segmentation, and conjoint analysis.

• Resume Optimization: Ensure your resume clearly details your 8+ years of experience, emphasizing ownership of full research projects from design to delivery, and prominently lists your proficiency in R, Python, and Qualtrics.

• Portfolio Preparation: Select 2-3 key projects that best demonstrate your expertise in survey design, advanced statistical analysis, and translating complex data into actionable insights. Prepare to present these projects in detail, focusing on methodology, challenges, and impact.

• Company Research: Familiarize yourself with Photon's initiative within Citi, understanding the goal of building an "AI-powered research intelligence system" and an "insights repository." Consider how your skills contribute to this strategic objective.

⚠️ 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.