Junior Data Analysis & Research Analyst

Sapience AI Corporation•Seattle, WA

22h•$78,000 - $85,000

About The Position

Sapience 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. Our 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. This 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. The 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. This 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. Strong 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. The 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. This 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. You 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. AI-augmented ways of working This 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. AI 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. The standard is human in partnership: AI helps carry the volume; you bring care, verification, context, empathy, and judgment. What this role is not To keep the boundary clear: 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. 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. 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. This is not a product management role. You do not own product delivery, specifications, sprint management, or the backlog. 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. This is not a role where speed replaces accuracy. Fast work is valuable only when the evidence, assumptions, and limitations are clear. What success looks like We measure this role on the quality and usefulness of the work produced: Accurate, traceable research. Important claims can be followed back to credible sources, and uncertainty is labeled rather than hidden. Decision-ready outputs. Research is organized, synthesized, and communicated in a form that helps the Lead Analyst and product team make progress. Reliable research systems. Airtable, Google Drive, and team trackers remain current, structured, searchable, and usable by others. Strong communication. Priorities, progress, findings, questions, and blockers are communicated clearly and at the right time. Human-centered analysis. Customer and member perspectives are represented accurately, respectfully, and with appropriate context. Sound analytical judgment. Over time, you become more effective at distinguishing fact from assumption, correlation from causation, and meaningful signal from noise. Consistent follow-through. Commitments are tracked, deadlines are respected, and work is completed with careful quality control. Growth and learning. You seek feedback, apply it, expand your technical and business fluency, and take on more complex assignments with increasing independence. How you work You lead with empathy. You try to understand the person, organization, and context behind the data before drawing conclusions. You communicate clearly. You adapt the level of detail to the audience, share progress without being chased, and raise risks before they become surprises. You listen actively. You ask thoughtful follow-up questions, capture what people mean accurately, and do not force their input into a predetermined answer. You are intellectually honest. You say when evidence is limited, correct mistakes quickly, and never make a claim sound stronger than the source supports. You are curious and resourceful. You look beyond the first answer, learn unfamiliar subjects, and use good judgment about when to investigate further or ask for help. You are dependable. You organize your work, follow through on commitments, and treat small details as part of the quality of the final analysis. You collaborate without ego. You share information, accept feedback, give credit, and care more about reaching the right answer than protecting your first answer. You are adaptable. You can work through ambiguity, changing priorities, and incomplete information while keeping stakeholders informed. You build trust. You handle sensitive information carefully, respect confidentiality, and treat colleagues, customers, members, and research participants with professionalism. Services & Tools Experience Google Workspace / Google Suite: Google Sheets, including formulas, pivot tables, lookups, and QUERY functions; Google Docs; Google Slides; Google Drive; and Gmail. Google Analytics: acquisition, engagement, event, conversion, audience, and performance reporting. SQL query environments: writing, adapting, troubleshooting, and validating queries; Google BigQuery or a comparable database or data-warehouse tool is beneficial. Airtable: data entry and quality control, tables, linked records, views, filters, forms, imports and exports, and basic automations. BI, survey, CRM, AMS, or data-visualization tools: hands-on experience with at least one platform and the ability to interpret and communicate its outputs. AI research and productivity tools used with source verification, privacy awareness, and human review. Web research, public-document review, and organized source capture. Prior Experience & Background Strong candidates will bring at least three years of progressively responsible professional experience. Relevant backgrounds include: Professional roles in business analysis, market research, product marketing, competitive intelligence, consulting, research operations, product operations, customer insights, or strategy. Business, nonprofit, association, or product research involving data collection, interviews, surveys, analysis, and written findings. Experience using SQL and analytical tools to answer business questions, validate data, or support recurring reporting. Experience maintaining a research database, project tracker, content library, CRM, AMS, or other structured operational system. Experience producing clear briefs, dashboards, presentations, comparison matrices, or recommendations for cross-functional or senior stakeholders. Experience supporting a Lead Analyst, senior researcher, product leader, or cross-functional team in a fast-moving environment. Cross-functional partners You work most closely with the Lead Analyst and Senior Product Marketer, and you collaborate with Product Management, Product Design, Engineering, Product Operations, and Research inside the product organization. You may also support work involving Marketing, Sales, Customer Success, and other go-to-market partners when research, product narrative, launch readiness, or adoption analysis requires cross-functional input. How we hire We review every application, and we encourage you to apply even if you do not match every line above. Research shows that talented people, especially those from underrepresented communities, often hold back when they do not meet every qualification. If that is the only thing holding you back, apply anyway. Sapience AI is an equal opportunity employer. We are committed to a workplace where everyone, regardless of background, has a voice in building what comes next.

Requirements

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.
Demonstrated ability to write, adapt, and troubleshoot SQL queries to extract, filter, join, aggregate, and validate data.
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.
Working proficiency with Google Analytics, including interpreting acquisition, engagement, event, and conversion data and translating the results into clear business insights.
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.
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.
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.
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.
Demonstrated attention to detail, source quality, dates, definitions, data integrity, and the difference between evidence and assumption.
The ability to manage multiple assignments, confirm priorities, meet deadlines, and communicate early when requirements or timelines are unclear.
Curiosity, empathy, active listening, and respect for people whose experiences or perspectives differ from your own.
Openness to feedback and the self-awareness to revise your work without defensiveness.

Nice To Haves

Experience conducting desk research, literature reviews, market scans, competitor analysis, customer research, survey analysis, or qualitative coding.
Familiarity with B2B software, artificial intelligence, product strategy, associations, membership organizations, professional communities, or knowledge-management platforms.
Experience turning research and data into a concise brief, presentation, dashboard, comparison matrix, or recommendation for business or product leaders.
Exposure to market sizing, segmentation, pricing research, product analytics, statistics, experimentation, or business-case development.
Experience connecting evidence across Google Analytics, SQL outputs, survey data, CRM or AMS records, and qualitative research.
Experience with Google BigQuery or another cloud data warehouse or SQL query environment.
Responsible hands-on use of AI tools for research, synthesis, or drafting, paired with clear verification practices.

Responsibilities

Conduct structured research on professional communities, associations, expert networks, AI platforms, enterprise software, and adjacent markets.
Gather and organize market-sizing inputs, segment characteristics, industry trends, customer needs, and signals that may affect Sapience AI’s product strategy.
Support the development of ideal customer profiles, personas, use cases, market landscapes, and opportunity assessments.
Monitor changes in the market and surface relevant developments to the Lead Analyst with the source, date, context, and level of confidence clearly stated.
Prepare research briefs, evidence tables, comparison matrices, and background materials for roadmap discussions and strategic planning.
Help translate market and customer research into clear problem statements, use cases, assumptions, dependencies, and questions that need validation.
Map customer needs and organizational pain points to product capabilities without overstating what the evidence supports.
Track decisions, open questions, supporting evidence, and follow-up research so the reasoning behind product choices remains visible and reusable.
Collect the words buyers, members, competitors, and industry leaders use to describe their problems, priorities, and desired outcomes.
Support the Lead Analyst and Senior Product Marketer in developing audience-specific messaging, proof points, feature-to-value mapping, and solution-level narratives.
Maintain a current library of verified claims, supporting evidence, customer language, definitions, and source citations.
Create clear first drafts of research summaries, internal documents, presentation content, and product-strategy materials for review.
Support customer, buyer, sponsor, and member research by preparing background briefs, interview guides, discussion questions, and note-taking templates.
Attend interviews or discovery sessions when assigned, listen actively, capture what was said accurately, and separate direct evidence from interpretation.
Organize qualitative feedback into themes, patterns, unresolved questions, and representative language without erasing important differences between participants.
Approach research participants and internal partners with empathy, discretion, curiosity, and respect for their time and perspective.
Monitor competing and adjacent platforms for meaningful changes in capabilities, pricing, packaging, partnerships, positioning, target customers, and product direction.
Maintain structured competitor profiles and comparison records in Airtable, including source links, publication dates, evidence type, confidence level, and last-reviewed date.
Conduct defined product and documentation reviews under the direction of the Lead Analyst, identifying similarities, differences, strengths, risks, and unanswered questions.
Escalate potentially important findings quickly and explain why they may matter, rather than only forwarding information without context.
Collect and normalize publicly available pricing, packaging, contract, feature-access, and buyer-segmentation information where it can be verified.
Support comparison of competitor offerings while documenting important differences that make direct comparisons imperfect.
Assist with research on willingness to pay, perceived value, adoption barriers, and the outcomes organizations and members expect from the product.
Maintain clear records of assumptions, calculations, source limitations, and confidence so pricing and value-model work can be reviewed and updated responsibly.
Support launch-readiness research by organizing target-audience needs, competitive context, proof points, anticipated objections, and success measures.
Use Google Analytics and other approved product or operational data sources to track acquisition, activation, adoption, engagement, retention, usage, and qualitative feedback.
Write and adapt SQL queries to extract, filter, join, aggregate, and validate data for analysis and reporting.
Build clear tables, charts, trackers, and summaries that show what changed, what may be driving the change, and what still cannot be concluded.
Prepare recurring updates that help the Lead Analyst and cross-functional partners see progress, risks, gaps, and recommended next questions.
Maintain Airtable bases, Google Drive folders, Google Sheets trackers, source libraries, templates, naming conventions, and version-control practices used by the analysis team.
Clean, standardize, deduplicate, and quality-check data before it is used in analysis or shared with stakeholders.
Keep research records current, searchable, and traceable so another team member can understand where a finding came from and when it was last verified.
Manage assigned work reliably by confirming priorities, tracking deadlines, communicating blockers early, and closing the loop when work is complete.
Improve repeatable workflows and documentation when you see a responsible way to make the team faster, clearer, or more accurate.