The strongest self-petition candidates rarely search for an attorney. Their public professional record already carries petition-grade material — awards, patents, publications, distinguished roles at globally recognized companies — accumulated as a side effect of doing the work. They do not type "EB-1A attorney" into a search box; their careers qualify quietly. This cohort finds those records at scale: a pedigree gate selects the pool, evidence boosters re-rank it, and every row ships with its evidence tags attached.
shipped 2026-06 series · MEASURED · Outricher data
EB-1A and NIW self-petition practices. Inbound brings the candidates who already suspect they have a case; this cohort is the outbound complement — the people with the strongest evidence density in the pool, ranked, most of whom have never spoken to an immigration lawyer. The evidence tags arrive pre-attached, so the first conversation starts from the record, not from a cold pitch.
Undergraduate degree from a top home-country institution family — premier engineering institutes and their peers — crossed with tenure at globally recognized product companies and 10+ years of experience. Three conditions, all structural, all readable from the professional-profile graph.
→ selects the poolAwards, patents, publications and active open-source footprint, extracted per profile and attached as tags. Measured coverage on the qualified pool: awards 22.1% · publications 13.2% · patents 8.5%. Boosters never filter — they re-rank, so a thin-evidence senior is demoted, not silently dropped.
→ re-rank onlyEB-1A and NIW are self-petitioned — no employer-sponsor signal is needed, so the cohort carries none. What it carries instead is evidence density plus sustained seniority: the shape adjudicators read, assembled before the candidate knows they will need it.
→ EB-1A · NIWTwenty real rows from the 2,788-lead delivery, ranked by evidence density. Names and most contact details are masked on this public page — the full rows, with names and verified emails, ship in the dataset (and a free 10-profile sample is one email away).
| Person | Role | Company | Undergrad (home country) | Graduate school | Experience | Evidence signals | |
|---|---|---|---|---|---|---|---|
| A. K. | Senior Machine Learning Engineer | Apple | National Institute of Technology Karnataka | Carnegie Mellon University | 16 yrs (7 US) | awards 9 · patents 2 · publications 4 · github 28 | a***@gmail.com |
| M. W. | Silicon Validation Software Engineer | Apple | Bhartiya Vidya Bhavans Sardar Patel Institute of Technology Munshi Nagar Andheri Mumbai | Stanford University School of Engineering | 10 yrs (4 US) | awards 1 · patents 1 · publications 3 · github 5 | w***@gmail.com |
| A. G. | Senior Data Scientist | Aptean | West Bengal University of Technology, Kolkata | Faculty of Engineering, University of Alberta | 14 yrs (5 US) | awards 5 · patents 1 · publications 20 | a***@***.com |
| S. S. | Applied Scientist | Amazon | Anna University | University of Melbourne | 19 yrs (6 US) | awards 2 · patents 2 · publications 1 | s***@gmail.com |
| B. S. | Data and Applied Scientist | Microsoft | Jadavpur University | University of Minnesota-Twin Cities | 13 yrs (7 US) | awards 7 · patents 3 · publications 18 | b***@***.ca |
| R. S. | Applied Scientist II | Amazon | Jadavpur University | Indian Institute of Management Bangalore | 12 yrs (4 US) | awards 12 · patents 1 · publications 9 | r***@***.com |
| S. G. | Data Scientist Senior Manager- GenAI Lead | Accenture AI | Savitribai Phule Pune University | Gies College of Business - University of Illinois Urbana-Champaign | 19 yrs (2 US) | awards 5 · publications 4 · github 42 | g***@gmail.com |
| A. G. | Senior Data Scientist | Nielsen | JNTUH College of Engineering Hyderabad | University of Central Missouri | 10 yrs (7 US) | awards 2 · patents 1 · publications 1 | a***@***.com |
| A. P. | Software Engineer | Delhi Technological University | University of Wisconsin-Madison | 11 yrs (4 US) | awards 5 · patents 2 · publications 3 | a***@***.com | |
| P. T. | Machine Learning Engineer | Adobe | Vellore Institute of Technology | Carnegie Mellon University | 10 yrs (7 US) | awards 3 · publications 3 · github 53 | p***@***.com |
| F. S. | Software Engineer | Apple | Thapar Institute of Engineering & Technology | Virginia Tech | 10 yrs (7 US) | awards 2 · patents 1 · publications 2 · github 6 | f***@***.com |
| S. S. | Software Engineer | Microsoft | National Institute of Technology Hamirpur | University at Buffalo | 16 yrs (7 US) | awards 3 · patents 4 · publications 3 · github 7 | s***@gmail.com |
| N. A. | Senior Software Engineer | NVIDIA | Indian Institute of Technology, Indore | University of Michigan | 13 yrs (6 US) | awards 5 · patents 12 · publications 3 | n***@***.de |
| S. D. | Software Engineer | Anna University | — | 16 yrs (5 US) | awards 2 · patents 1 · publications 3 | s***@gmail.com | |
| A. D. | Software Engineer | University of Pune | University of California, Santa Cruz | 13 yrs (6 US) | awards 5 · patents 3 · publications 6 · github 10 | a***@***.com | |
| A. B. | Software Engineer | Meta | Jadavpur University | University of Wisconsin-Madison | 11 yrs (6 US) | awards 10 · patents 1 · publications 1 | a***@gmail.com |
| V. P. | Data Scientist | Intel Corporation | R. V. College of Engineering, Bangalore | Arizona State University | 10 yrs (7 US) | awards 2 · patents 2 · publications 7 | v***@***.com |
| A. G. | Senior Data Scientist | SAP | University of Mumbai | International School of Engineering (INSOFE) | 13 yrs (7 US) | awards 2 · patents 1 · publications 1 | a***@gmail.com |
| P. K. | Software Engineer | Meta | Indian Institute of Technology, Delhi | Indian Institute of Technology, Delhi | 12 yrs (5 US) | awards 2 · patents 7 · publications 2 · github 19 | p***@***.com |
| H. R. | Senior Software Engineer | Snowflake | Birla Institute of Technology, Mesra | Stony Brook University | 10 yrs (7 US) | awards 7 · publications 2 · github 36 | h***@***.com |
Rows are real and drawn from the shipped delivery. Masking on this page: names reduced to initials, email domains hidden except free providers. The full rows — names, verified emails, all 137 average fields — ship in the dataset.
The pedigree pass scanned 4,415,891 profiles across the professional-profile graph → 507,535 US-based in target roles → 69,551 after the home-country undergraduate prefilter → 31,705 qualified → 2,788 shipped. Funnel selectivity 25:1, and every drop reason is named in the delivery artifacts. Email coverage on this cohort is 48.3% — evidence-dense seniors guard their contacts — and we quote that number honestly: every address is verified-or-empty, none are guessed.
The honesty boundary
Evidence tags mark that evidence may exist at petition grade — counting awards is not adjudicating them. Citation counts and patent grant status are not observable in the profile record, and we say so in the dataset README. The attorney qualifies; the dataset ranks propensity, attaches the evidence, and lets intake start from the record instead of from zero.
Two denominators, stated plainly: the funnel percentages above describe the qualified pool of 31,705; everything below is measured on the shipped delivery (2,778 cleanly-parsed rows of 2,788).
Lower bounds: evidence extraction ran on the 2,431 enrichment-matched rows (87.5%) — the rest may hold more. Median award-holder carries 2 awards (max 19); the deepest patent stack is 27. Twenty-three people carry all three asset classes.
State known for 2,153 rows (77.5%) — geography cuts for a state-licensed practice are a delivery option, not an afterthought.
Median 12 years of experience (the 10-year floor is a spec gate, not an accident), median 6 years already in the US. Architect-titled rows run deepest at a median 18 years.
73.3% sit inside 10,000+-employee companies — invisible to intent tools, which is precisely why this pool is uncontested.
1,572 hold a master's degree, 41 a PhD — the classic home-country-undergrad → US-graduate-school arc, on record for every row. Undergraduate side: 100% top home-country institution families, by gate.
49.0% email on the shipped set — evidence-dense seniors guard their contacts, and we publish the real number. Freshness holds: 85.6% of profiles updated within 12 months; the records are current, not archival.
Everything above ships as a file you own — and a free 10-profile sample matched to your practice comes first.
A full delivery contains:
Each row is the complete data object — career, education, evidence, contacts, scoring. See what one contact looks like, field by field →
AI outreach agents can draft the first touch straight from these fields — referenced to each person's actual career, so the mail lands with a real person, not a template.
Re-runs are deduplicated against everything previously delivered to you and can be narrowed by geography, discipline or evidence emphasis. Datasets $0.19–0.42 per contact; managed outreach and API delivery available.
Or just write — a consultation is free: we will map this cohort to your practice.
Outricher · Visa Talent Engine · shipped 2026-06 series · MEASURED · Outricher data