Junior Data Analysis & Research Analyst

Job DescriptionJob Description:\n\nAbout Sapience AI\nSapience AI is the collective intelligence platform for professional communities. We sit above the CRMs, AMS platforms, and knowledge bases that organizations already run, and we turn the expertise scattered across them into something every member can search, act on, and share.\nThe intelligence a community needs is already inside it. Most organizations just cannot reach it. Knowledge lives in silos, in legacy systems, in the heads of a few experts, and in fragmented records no one can connect. We change that.\nOur work is grounded in four commitments: technology elevates people and never replaces them, the best expertise is already inside the community, everything is built on trust, and every deployment is purpose-driven for the organization it serves.\nLet's achieve more, together.Where this role sits\nThis is an analysis and research role inside the core product organization. It works closely with the Lead Analyst and Senior Product Marketer to bring credible market, customer, competitor, and product evidence into the decisions Sapience AI makes.\nThe Junior Data Analyst is the evidence and research engine behind the team's work. You will gather information, evaluate sources, structure data, maintain research systems, identify patterns, and turn complex material into clear first-draft findings. Your work will support market and category strategy, product decisions, positioning, competitive intelligence, pricing and packaging research, launches, and adoption analysis.\nThis is a junior-level professional role with meaningful responsibility. It is intended for someone with at least three years of relevant experience; it is not an internship or a first professional role. You will be expected to independently own well-scoped assignments, ask thoughtful questions, communicate clearly, work with care, and apply sound judgment when distinguishing a useful signal from incomplete or misleading information. Final strategic accountability remains with the Lead Analyst and relevant product leaders.Why this role exists\nStrong strategic analysis depends on disciplined research underneath it. Market signals are scattered across customer conversations, product documentation, public filings, industry reports, competitor releases, usage data, and the language organizations use to describe their needs. Without a reliable system for collecting, validating, and organizing those signals, senior analysts spend too much time rebuilding the evidence and not enough time interpreting it.\nThe Junior Data Analyst multiplies the effectiveness of the Lead Analyst and the wider product team. You make the research base more complete, current, traceable, and usable. You help turn raw information into decision-ready evidence, while protecting the quality standards that make the team's conclusions defensible.\nThis role also helps Sapience AI keep the human at the center of its analysis. Data matters, but so do context, lived experience, and the language people use to explain what is difficult, valuable, or missing. Empathy and analytical rigor are not separate skills here. We need both.What you will contribute (Areas of Responsibility)\nYou will support eight connected areas of work. Your responsibility is to independently execute well-scoped assignments, apply sound analytical judgment, maintain high research standards, and make the Lead Analyst and product team more effective.1. Market and category research\n\n Conduct structured research on professional communities, associations, expert networks, AI platforms, enterprise software, and adjacent markets.\n Gather and organize market-sizing inputs, segment characteristics, industry trends, customer needs, and signals that may affect Sapience AI's product strategy.\n Support the development of ideal customer profiles, personas, use cases, market landscapes, and opportunity assessments.\n Monitor changes in the market and surface relevant developments to the Lead Analyst with the source, date, context, and level of confidence clearly stated.\n2. Product strategy and roadmap support\n\n Prepare research briefs, evidence tables, comparison matrices, and background materials for roadmap discussions and strategic planning.\n Help translate market and customer research into clear problem statements, use cases, assumptions, dependencies, and questions that need validation.\n Map customer needs and organizational pain points to product capabilities without overstating what the evidence supports.\n Track decisions, open questions, supporting evidence, and follow-up research so the reasoning behind product choices remains visible and reusable.\n3. Positioning and product narrative support\n\n Collect the words buyers, members, competitors, and industry leaders use to describe their problems, priorities, and desired outcomes.\n Support the Lead Analyst and Senior Product Marketer in developing audience-specific messaging, proof points, feature-to-value mapping, and solution-level narratives.\n Maintain a current library of verified claims, supporting evidence, customer language, definitions, and source citations.\n Create clear first drafts of research summaries, internal documents, presentation content, and product-strategy materials for review.\n4. Discovery, validation, and the voice of the community\n\n Support customer, buyer, sponsor, and member research by preparing background briefs, interview guides, discussion questions, and note-taking templates.\n Attend interviews or discovery sessions when assigned, listen actively, capture what was said accurately, and separate direct evidence from interpretation.\n Organize qualitative feedback into themes, patterns, unresolved questions, and representative language without erasing important differences between participants.\n Approach research participants and internal partners with empathy, discretion, curiosity, and respect for their time and perspective.\n5. Competitive and product intelligence\n\n Monitor competing and adjacent platforms for meaningful changes in capabilities, pricing, packaging, partnerships, positioning, target customers, and product direction.\n Maintain structured competitor profiles and comparison records in Airtable, including source links, publication dates, evidence type, confidence level, and last-reviewed date.\n Conduct defined product and documentation reviews under the direction of the Lead Analyst, identifying similarities, differences, strengths, risks, and unanswered questions.\n Escalate potentially important findings quickly and explain why they may matter, rather than only forwarding information without context.\n6. Packaging, pricing, and value-model research\n\n Collect and normalize publicly available pricing, packaging, contract, feature-access, and buyer-segmentation information where it can be verified.\n Support comparison of competitor offerings while documenting important differences that make direct comparisons imperfect.\n Assist with research on willingness to pay, perceived value, adoption barriers, and the outcomes organizations and members expect from the product.\n Maintain clear records of assumptions, calculations, source limitations, and confidence so pricing and value-model work can be reviewed and updated responsibly.\n7. Launch, adoption, and performance analysis\n\n Support launch-readiness research by organizing target-audience needs, competitive context, proof points, anticipated objections, and success measures.\n Use Google Analytics and other approved product or operational data sources to track acquisition, activation, adoption, engagement, retention, usage, and qualitative feedback.\n Write and adapt SQL queries to extract, filter, join, aggregate, and validate data for analysis and reporting.\n Build clear tables, charts, trackers, and summaries that show what changed, what may be driving the change, and what still cannot be concluded.\n Prepare recurring updates that help the Lead Analyst and cross-functional partners see progress, risks, gaps, and recommended next questions.\n8. Research operations, data quality, and team enablement\n\n Maintain Airtable bases, Google Drive folders, Google Sheets trackers, source libraries, templates, naming conventions, and version-control practices used by the analysis team.\n Clean, standardize, deduplicate, and quality-check data before it is used in analysis or shared with stakeholders.\n Keep research records current, searchable, and traceable so another team member can understand where a finding came from and when it was last verified.\n Manage assigned work reliably by confirming priorities, tracking deadlines, communicating blockers early, and closing the loop when work is complete.\n Improve repeatable workflows and documentation when you see a responsible way to make the team faster, clearer, or more accurate.\nAI-augmented ways of working\nThis role is built for how product and research teams work in 2026. You will use AI to accelerate source discovery, organize information, classify findings, compare documents, identify possible patterns, and develop first drafts.\nAI output is never treated as evidence by itself. You are responsible for tracing factual claims back to credible original sources, checking dates and context, preserving source links, labeling uncertainty, and escalating anything you cannot confirm. You will also follow Sapience AI's privacy, confidentiality, and responsible-use standards when working with internal, customer, or research-participant information.\nThe standard is human in partnership: AI helps carry the volume; you bring care, verification, context, empathy, and judgment.What this role is not\nTo keep the boundary clear:\n\n This is not an administrative-assistant role. Some coordination and documentation are part of the work, but the purpose of the role is analysis, research, and insight development.\n This is not an internship or trainee role. The person hired will bring at least three years of relevant professional experience and be ready to own defined analytical work with limited oversight.\n This is not the final strategic decision-maker. You will contribute evidence and increasingly strong interpretation; the Lead Analyst and relevant product leaders retain accountability for final strategic recommendations and decisions.\n This is not a product management role. You do not own product delivery, specifications, sprint management, or the backlog.\n This is not a brand, demand-generation, or sales role. You may support product narrative and launch readiness, but campaigns, lead generation, and the sales motion are owned elsewhere.\n This is not a role where speed replaces accuracy. Fast work is valuable only when the evidence, assumptions, and limitations are clear.\nWhat success looks like\nWe measure this role on the quality and usefulness of the work produced:\n\n Accurate, traceable research. Important claims can be followed back to credible sources, and uncertainty is labeled rather than hidden.\n Decision-ready outputs. Research is organized, synthesized, and communicated in a form that helps the Lead Analyst and product team make progress.\n Reliable research systems. Airtable, Google Drive, and team trackers remain current, structured, searchable, and usable by others.\n Strong communication. Priorities, progress, findings, questions, and blockers are communicated clearly and at the right time.\n Human-centered analysis. Customer and member perspectives are represented accurately, respectfully, and with appropriate context.\n Sound analytical judgment. Over time, you become more effective at distinguishing fact from assumption, correlation from causation, and meaningful signal from noise.\n Consistent follow-through. Commitments are tracked, deadlines are respected, and work is completed with careful quality control.\n Growth and learning. You seek feedback, apply it, expand your technical and business fluency, and take on more complex assignments with increasing independence.\nWho you are Required qualifications\n\n Three or more years of professional experience in research, business analysis, market analysis, product strategy, product marketing, competitive intelligence, consulting, research operations, customer insights, or a closely related analytical role.\n Demonstrated ability to write, adapt, and troubleshoot SQL queries to extract, filter, join, aggregate, and validate data.\n Hands-on experience with at least one BI, survey, CRM, AMS, or data-visualization tool, including the ability to evaluate outputs and communicate findings to nontechnical stakeholders.\n Working proficiency with Google Analytics, including interpreting acquisition, engagement, event, and conversion data and translating the results into clear business insights.\n A bachelor's degree or equivalent practical experience in business, economics, marketing, market research, data analytics, information science, communications, psychology, sociology, political science, or another field that develops strong research and analytical skills.\n Strong written and verbal communication, including the ability to explain what you found, how you found it, what remains uncertain, and why it may matter.\n Strong working proficiency with Google Sheets and Google Workspace, including formulas, pivot tables, lookups, QUERY functions, data checking, and clear presentation of findings in Docs and Slides.\n Experience using Airtable or another structured data or work-management platform, with the ability to maintain clean, linked, traceable records and become productive in Airtable quickly.\n Demonstrated attention to detail, source quality, dates, definitions, data integrity, and the difference between evidence and assumption.\n The ability to manage multiple assignments, confirm priorities, meet deadlines, and communicate early when requirements or timelines are unclear.\n Curiosity, empathy, active listening, and respect for people whose experiences or perspectives differ from your own.\n Openness to feedback and the self-awareness to revise your work without defensiveness.\nPreferred qualifications\n\n Experience conducting desk research, literature reviews, market scans, competitor analysis, customer research, survey analysis, or qualitative coding.\n Familiarity with B2B software, artificial intelligence, product strategy, associations, membership organizations, professional communities, or knowledge-management platforms.\n Experience turning research and data into a concise brief, presentation, dashboard, comparison matrix, or recommendation for business or product leaders.\n Exposure to market sizing, segmentation, pricing research, product analytics, statistics, experimentation, or business-case development.\n Experience connecting evidence across Google Analytics, SQL outputs, survey data, CRM or AMS records, and qualitative research.\n Experience with Google BigQuery or another cloud data warehouse or SQL query environment.\n Responsible hands-on use of AI tools for research, synthesis, or draftin