Lead Researcher / Web Scraper

Research real casino brands from public affiliate directories (e.g. AskGamblers, Casino.guru) and SiGMA / ICE exhibitor & sponsor listings etc. Focus on offshore and grey-market brands (Curaçao, Anjouan, Costa Rica, US-facing, plus Asia-, India-, and LatAm-focused). Exclude brands licensed solely under UKGC, MGA, or Gibraltar. For each brand, capture: operating company, parent group, license jurisdiction, target markets, and languages. Pull contact details from the casino's own official site (Contact / Imprint / Support): general business email, partnerships/BD email if available, Telegram, LinkedIn (company + relevant decision-makers where public). Deduplicate by operating company — many brands share one operator (e.g. Araxio, Hollycorn, TechSolutions, Lynton). Do not list multiple skins of the same operator as separate leads. Record a traffic/popularity figure per brand (e.g. SimilarWeb estimate) and rank the list by it. Document every row with source_url and date_scraped. Deliverable A CSV in the provided 19-column schema: brand_name, operating_company, parent_group, license_jurisdiction, primary_markets, languages, similarweb_monthly_visits, traffic_rank, website_url, contact_page_url, email_general, email_partnerships, telegram, linkedin_company, linkedin_contact, source_url, date_scraped, confidence_flag, segment Plus a short notes file covering: sources used, low-confidence rows, and any sites skipped for ToS reasons. Quality & compliance (important) No invented brands, domains, or contacts. Every row must be a real, existing casino, verifiably sourced. We would rather have 480 real rows than 500 with filler. This is the single most important rule of the project. Respect robots.txt and each site's terms of service; use rate-limiting and an identifiable User-Agent. Only business contact data from official pages — no aggregated or purchased personal data. Required skills Python (requests/BeautifulSoup, or Playwright/Scrapy); experience with JS-rendered pages. Strong data hygiene: deduplication, normalization, clean CSV output. Accuracy and diligence; written and spoken English. Nice to have iGaming / affiliate-ecosystem knowledge. Email validation experience (syntax + MX/deliverability checks). Please answer in your application (screening) How do you distinguish a "real but unverified" row from a fabricated one — and how do you verify it? What do you do when an affiliate directory prohibits scraping in its terms of service? Name 3 offshore casino brands you would not include, and why.