B2B Data Research Specialist – Manual List Building for US Market Expert-Level Research | No AI Responses | Paid Test Required Overview We are seeking an experienced B2B data research specialist for ongoing, high-volume list building targeting US-based executives. This is manual, judgment-intensive research work—not data scraping, not Apollo/ZoomInfo exports, not AI-generated lists. The ideal candidate has deep experience navigating LinkedIn, company websites, news sources, and business databases to identify, verify, and score potential buyers based on specific criteria. You will receive detailed targeting specifications and deliver fully researched, verified records with multiple data points per contact. This engagement begins with a series of paid tests. Compensation increases with demonstrated quality and volume capacity. What This Role Requires • Manual research judgment: You will need to evaluate sources, cross-reference information, and make decisions about data quality. This cannot be automated. • LinkedIn fluency: Deep familiarity with LinkedIn profiles, company pages, job history interpretation, and tenure calculation from role start dates. • Web research skills: Ability to verify company ownership (PE-backed, family-owned, founder-led), identify decision-makers, and find signals from press releases, news, and company websites. • US market familiarity: Understanding of US business structures, private equity ownership, Fortune 500 companies, and executive titles. • Attention to detail: Every record must be verified. We score on accuracy, not just volume. • Clear written communication: You must be able to explain your research process, flag ambiguous cases, and ask clarifying questions in plain English. What This Role Is NOT • Not data scraping or automation: We do not want Apollo, ZoomInfo, or Hunter exports. We need manually researched and verified records. • Not AI-assisted research: Do not use ChatGPT, Claude, or other AI tools to generate your responses or deliverables. We will detect it and end the engagement. • Not button-clicking execution: This is research and judgment work, not campaign sending or SDR execution. Example Data Points Per Record (15-20 fields) Each record will require some combination of: • Contact: Full name, title, LinkedIn URL, verified email • Company: Name, website, industry, revenue range, employee count • Ownership: PE sponsor name, transaction date, family-owned status, founder-led status • Tenure: Role start date, months in position (calculated from LinkedIn) • Signals: Hiring activity, recent funding, expansion news • Scoring: Multi-dimensional rating based on provided rubric (A/B/C/D) Engagement Structure 1. Paid Test Phase: You will receive a detailed targeting spec for a specific buyer persona. You will deliver 50-100 records. We will evaluate accuracy, research depth, and process. 2. Calibration: We will provide detailed feedback. Candidates who meet quality standards proceed. 3. Ongoing Engagement: Volume increases based on quality. Pricing is per-record, negotiated based on demonstrated capacity and accuracy. Long-term relationships with reliable partners. Requirements • 3+ years of B2B data research or lead generation experience • Demonstrated experience with US market targeting • LinkedIn Sales Navigator experience (preferred but not required) • Excellent written English—clear, direct, no jargon • Reliable availability for ongoing work (10-30+ hours/week potential) • Ability to follow detailed specifications and scoring rubrics Application Questions – Answer ALL in Your Proposal Your proposal must answer these questions directly. Generic proposals or AI-generated responses will be immediately rejected. 1. Walk me through your exact process for finding and verifying a CFO at a PE-backed company. Describe step-by-step: What sources do you use? In what order? How do you confirm PE ownership? How do you verify the person is still in role? How do you find their email? Be specific—tool names, search operators, verification methods. 2. How do you calculate tenure for an executive from their LinkedIn profile? Give a specific example. If someone's LinkedIn shows they started as CFO in "August 2024" and today is February 2025, what tenure do you report? What if it only says "2024" with no month? 3. You're researching a company and can't determine if it's PE-backed or family-owned. What do you do? Walk me through your research process for ambiguous ownership. What sources do you check? At what point do you flag it as unknown vs. making an assumption? 4. Name three specific challenges with researching US-based executives that don't exist when researching other markets. Be specific to the US market. This tests whether you've actually done this work. 5. What is your quality control process before delivering a batch of records? How do you catch errors? What's your accuracy target? What do you do when you're unsure about a data point? 6. What is your per-record pricing for 15-20 field manual research at 500-1000 records/month volume? Give a specific number. This is not negotiable upward after agreement. If your pricing changes based on seeing the targets, do not apply. 7. How many hours does it take you to research and deliver 100 fully verified records with 15-20 fields each? Be honest. This helps us understand your capacity and efficiency. 8. Provide a brief sample: Research "Acme Corp" (fictional) – a mid-market manufacturing company. In 3-4 sentences, describe what sources you would check and what signals you would look for to determine: (a) ownership structure, (b) the right executive contact, (c) whether they're actively hiring. This is a thought exercise—we want to see your research logic. What We Offer • Clear, detailed targeting specifications (you will not be guessing) • Fair, consistent pricing—we do not renegotiate after agreement • Fast payment on approved deliverables • Volume growth for reliable partners (1000+ records/month potential) • Direct communication—no layers, no bureaucracy • Long-term relationship for those who deliver AI Policy – Read Carefully Any use of AI (ChatGPT, Claude, etc.) for communication with us will result in immediate disqualification. We can handle honest people with less-than-perfect English. We will not handle anyone hiding behind AI to speak with us or engage. If you want to use AI for specific business purposes (e.g., email verification tools, data enrichment), that must be approved by us first. Using AI to draft your messages, proposals, or responses to us is not approved and never will be. We will detect it. We will end the engagement. No exceptions. FINAL NOTE: We value honesty and directness. If you're unsure whether you're qualified, ask a clarifying question before applying. If your proposal doesn't answer the specific questions above, it will be rejected without response. We are looking for a partner, not a vendor. Integrity matters more than polish.