Lead Research Automation — Company Data Enrichment & Contact Scoring

Here’s what I’m doing: I’m looking to make our lead research process better and get it fully automated. Here’s the process as it stands now: 1. We start with a list of companies and their websites. 2. For each one: scrape their site for shipping facility locations — warehouses, DCs, manufacturing plants. Check their locations/facilities pages and their careers page. Job postings for warehouse, forklift, shipping & receiving, production roles confirm an active facility and usually give the address. Careers pages are inconsistent — some are static HTML, some run through Workday, Greenhouse, iCIMS, or other ATS platforms that don’t always let scrapers in. I need someone who’s dealt with that before and knows how to handle it, not just scrape the easy ones and skip the rest. 3. Identify contacts matching these titles: Transportation Manager, Director of Transportation, Logistics Manager, Director of Logistics, Traffic Manager, Senior Transportation Manager, Senior Logistics Manager, Logistics Sourcing Manager, Logistics Procurement Manager, Transportation Procurement Manager, Transportation Sourcing Manager. 4. Score every contact for whether they’re actually still there — not just whether they show up in a database. Apollo and ZoomInfo are full of people who left or retired years ago but still show as active. The scorecard has to catch that before it goes any further. 5. Enrich the contacts that pass: email and phone. Phones: enriched numbers are usually garbage with no way to verify them. Scrape the company website for the corporate number instead. No 800 numbers — those are dead-end customer service lines. I want a local number so I can call and ask whether the person is still there. I know distinguishing a corporate number from an 800 number on a scraped page isn’t always straightforward — some sites only list the 800 number, some bury the local number in a footer or contact page. Tell me how you’d approach that. Emails: fine as-is — enrich, run through NeverBounce. Non-negotiable: every contact comes out with a score and the specific reason behind it. No black box. I need to see why someone scored high and why someone else scored low. End state: I drop in the list, it runs, output comes back scored and enriched with facility locations attached. Fully automated, repeatable.