Datensatz zu 372000 US-Fachkraeften mit indischem Arbeitgeber in der Vergangenheit erstellt

Part of the series "GTM workflows with Apify and Claude". The series opener explains the stack and the four ways to run everything below; this part builds the third list and is the one where the data pushed back hardest, so it doubles as a lesson in checking a signal before you trust it.

Cross-border fintech sells to a moment: someone has recently started working in a new country, earns in one currency and has obligations in another. Remittance apps, NRI banking, international health and life insurance, relocation and tax services all want the same people in the same six months. Ad platforms offer interests and demographics. Neither says "started a job in Dallas last spring after six years at an IT services firm in Bengaluru".

Career data says exactly that, because a profile carries employers with locations and dates. The LinkedIn People Search Actor on Apify exposes it as two fields: pastEmployerCountries (worked at a company headquartered in these countries) and leftPastEmployerAfterYear, which you combine with where the person lives now and when the current role started. The count and market modes return aggregates and no rows, so I could try several definitions before pulling anything.

A note on use before the numbers. This is professional history, not nationality or origin, and that is how it should be used: to find an audience for a consumer product, under the ad platform's customer-list terms and your own privacy basis. Do not use it for employment decisions; in the US that would be national-origin discrimination.

{ "mode": "count", "countries": [ "us" ], "pastEmployerCountries": [ "in" ], "currentRoleStartedAfterYear": 2025, "employeeCountMin": 50 } Enter fullscreen mode Exit fullscreen mode 371,998 people. Then mode: "market", 188 aggregate rows. The top employers were Deloitte (15,010 positions), Google, Amazon, Meta, Microsoft, Apple, EY, AWS, JPMorgan, Capital One, Adobe, Infosys. Software was 19% of positions, IT services 11%, Financial services 10%. And Higher Education was 13%, with "research assistant", "graduate research assistant" and "graduate teaching assistant" in the top titles.

That is the market view doing its job: the first definition had pulled in graduate students who had a job in India before a US degree. One exclusion list fixed it, and I ran ten variants as segments in one call, counts only, to see what else was in there:

City slices on the first definition, counts only: New York 40,873, Bay Area 38,631, Dallas–Fort Worth 13,992, Seattle 13,850, Chicago 12,433, Atlanta 8,043, Austin 7,984. Engineers 102,505. Personal email on record: at most 36%.

Before pulling rows, I wanted to know what pastEmployerCountries: ["in"] matches. The field matches the headquarters country of the employer record, not where the person sat. For a company that only exists in India, those are the same thing. For a multinational, a person may have worked at the Indian entity, or the company record itself may resolve to an Indian headquarters.

So I pulled full profiles for twelve people from the engineer slice with Profile Lookup and read the positions. Four of the twelve had a position located in India on the record (Zomato in Gurgaon, Microsoft in Hyderabad, Infosys and GE in Bengaluru, two Pune firms). The other eight matched a company record headquartered in India without a visible India stint on the profile.

That is the honest state of the broad count: it is an upper bound for "built a career in India and now works in the US". It is still a useful audience for awareness and lookalikes, but for a list you will actually contact, tighten it. Two ways worked.

Add leftPastEmployerAfterYear: 2025. Now the person left an India-based employer in 2025 or later and started a US role in 2025 or later. 30,812 people.

The market view of that slice looks like the audience a remittance product describes in its own deck: top employers Deloitte (26% of positions), Infosys, HCLTech, Tata Consultancy Services, Wipro, Persistent, Cognizant, PwC, LTIMindtree, EY, Tech Mahindra. Industries: consulting 30%, IT services 21%, software 9%, financial services 7%. Employers with 10,001+ staff: 64%. Top titles: senior consultant, manager, consultant, senior manager, software engineer, audit and assurance senior, tax manager.

Cities: New York 3,159, Dallas–Fort Worth 1,555, Bay Area 1,548, Chicago 1,208, Seattle 647. Engineers 5,007, manager and above 9,349. Personal email on record: at most 30%.

One caveat inside the caveat: Deloitte at 26% is almost certainly the Indian member firm's staff moving to US engagements. That is a real cross-border move, and also a pattern of one company's records. If Deloitte matters to your plan, spot-check a few profiles before you count on it.

The sharpest definition does not use the country field at all. Ten Indian IT services companies by domain:

{ "mode": "count", "countries": [ "us" ], "pastEmployerDomains": [ "infosys.com", "tcs.com", "wipro.com", "hcltech.com", "techmahindra.com", "ltimindtree.com", "persistent.com", "mphasis.com", "hexaware.com", "zensar.com" ], "segments": [ { "name": "any time" }, { "name": "left 2025+", "leftPastEmployerAfterYear": 2025 }, { "name": "left 2025+, US role started 2025+", "leftPastEmployerAfterYear": 2025, "currentRoleStartedAfterYear": 2025 }, { "name": "left 2024+, US role started 2024+", "leftPastEmployerAfterYear": 2024, "currentRoleStartedAfterYear": 2024 }, { "name": "left 2025+, engineers", "leftPastEmployerAfterYear": 2025, "titleKeywords": [ "engineer", "developer", "data", "architect" ]}, { "name": "left 2025+, manager+", "leftPastEmployerAfterYear": 2025, "seniority": [ "manager", "director", "vp", "cxo" ]}, { "name": "left 2025+, Dallas", "leftPastEmployerAfterYear": 2025, "localityKeywords": [ "dallas", "plano", "irving", "frisco" ]}, { "name": "left 2025+, NY/NJ", "leftPastEmployerAfterYear": 2025, "localityKeywords": [ "new york", "jersey city", "edison", "new jersey" ]} ] } Enter fullscreen mode Exit fullscreen mode Segment (US residents, alumni of the ten firms) People Any time 461,226 Left in 2025 or later 8,561 Left 2025+, US role started 2025+ 5,495 Left 2024+, US role started 2024+ 15,150 Left 2025+, engineering and data titles 2,354 Left 2025+, manager and above 1,518 Left 2025+, Dallas–Fort Worth 691 Left 2025+, New York and New Jersey 900 Left 2025+, Bay Area 366 Where the 8,561 went: Cognizant, Capgemini, Citi, JPMorgan, Fidelity, Microsoft, Charles Schwab, Amazon, Deloitte, CVS Health, Bank of America. IT services 19%, financial services 9%, software 7%, healthcare and insurance 5%. Personal email on record: at most 31%.

These are people who left an India-headquartered IT firm within the last two years and now work for a US bank, insurer or tech company. For a remittance or NRI banking product that is the list.

All of this was counts and twelve profiles: three definitions, twenty-nine segments, one spot check, no list pulled yet.

To see the rows I pulled 1,000 from the broad definition narrowed to engineering and data titles:

{ "mode": "people", "profileDetail": "row", "countries": [ "us" ], "pastEmployerCountries": [ "in" ], "currentRoleStartedAfterYear": 2025, "employeeCountMin": 50, "titleKeywords": [ "engineer", "developer", "data" ], "maxResults": 1000 } Enter fullscreen mode Exit fullscreen mode Run log: 3 minutes 21 seconds, 1,000 rows delivered, 50 of them older snapshots of a person already in the run, recognised and marked by the Actor.

For an ads team the location and employer-size columns are the media plan. For a sales team selling payroll or benefits to employers of this population, companyDomain grouped and counted is the account list.

Ad platforms match customer lists on email, and for a consumer product the personal mailbox is the one that matches. The search has a tier for that: profileDetail: "contacts" with mustHave: ["personalEmail"] delivers only the people who have one.

{ "mode": "people", "profileDetail": "contacts", "compact": true, "mustHave": [ "personalEmail" ], "countries": [ "us" ], "pastEmployerCountries": [ "in" ], "currentRoleStartedAfterYear": 2025, "employeeCountMin": 50, "titleKeywords": [ "engineer", "developer", "data" ], "excludeTitleKeywords": [ "student", "assistant", "intern", "fellow" ], "maxResults": 200 } Enter fullscreen mode Exit fullscreen mode Run log: 73 seconds, 200 people delivered, 98 skipped for having no personal email. Of the 200: personal email 199, work email 185 (65 at the current employer), a direct phone 72, social links 119. Each record also carries the five latest positions with dates and the education list, which is how I did the spot check above without a second pull.

What I would do with the three definitions:

The counts are the part that took the time; each pull was minutes.

Swap the country code and the same input produces a different market. Counts on 2026-10-10 for people in the US whose current role started in 2025 or later, by the headquarters country of a past employer: United Kingdom 663,262, China 99,749, Brazil 59,770, Philippines 19,153. The same leftPastEmployerAfterYear and domain-list tightenings apply. For a product sold in the other direction (people who moved out of the US), swap countries and pastEmployerCountries.

The same query inverted is a map of people who made your move before you. Put your target employer in companyDomains, keep pastEmployerCountries: ["in"] (or your own country), and you have the colleagues at that company who also came from a company back home. Those are the warm introductions a job board cannot give you, and a mode: "count" on it tells you how many before you write to anyone.

The two fields behind this article, pastEmployerCountries and leftPastEmployerAfterYear, answer more questions than remittances:

"Use b2bsearch/people-database-search. I want people living in the US who used to work at a company headquartered in India and whose current role started in 2025 or later, at employers with 50+ staff. Run mode count, then mode market. Read the market rows and tell me if anything in the top titles or industries suggests the filter is catching the wrong people; propose exclusions. Then run one segments call comparing: the refined filter, refined plus leftPastEmployerAfterYear 2025, and refined by industry. Do not run mode people until I confirm."

The agent flagged the graduate students from the market rows, proposed the exclusion list, and ran the comparison. Two of the three definitions in this article came out of that exchange.

Actor b2bsearch/people-database-search. pastEmployerCountries matches the headquarters country of a past employer's record, not the person's past location; treat broad counts as an upper bound and tighten with leftPastEmployerAfterYear or an explicit pastEmployerDomains list of companies that exist only in that country. localityKeywords filters current location by city or region text. mustHave: ["personalEmail"] on profileDetail: "contacts" delivers only people with a personal mailbox; people without one are skipped. Per-tier pricing is on the Actor page; run mode: "count" and report the number before any people run. segments compares up to ten filter variants in one mode: "count" run. Full profiles for a spot check: b2bsearch/profile-lookup, input profileUrls, positions are under experience.work with location, startDate, endDate.

Use professional history for consumer-marketing audiences only, never for employment decisions.

This closes the three-list cycle. The earlier parts built the ex-Big-Tech founders list and the new-leaders list; all three run from the same Actor, the same four ways, each sized with counts before a single row was pulled.

Next in the series: a spend gate for the agent, the hook that keeps an agent from pulling 25,000 rows because that was the default.

If you run the corridor for your own market, post the count and the top employer in the comments. Corridor by corridor, that is the map nobody has published.