What FY2025 filing data shows
Our analysis of the DOL OFLC disclosure files found 7,410 certified H-1B LCAs in the data-scientist title family across 2,361 employers in FY2025. The median offered wage was $137,500. The largest grouped counts were Amazon (386), Meta (338) and Walmart Associates (336).
Method: cases are de-duplicated by case number and restricted to certified H-1B LCAs. Title families match configured word sequences in order within the filed job title, so intervening words can appear. Wages use WAGE_RATE_OF_PAY_FROM, are annualized by pay unit, exclude annualized values below $20,000 and are rounded to the nearest $500. Amazon, Google, Meta, Microsoft and Apple are rolled up to their parent; other legal entities remain separate. Adjacent titles discussed below are not silently counted as data scientists. An LCA is not a petition approval, hire, open job or promise to sponsor. Data downloaded 6 November 2026; current openings are on the data scientist sponsor page.
Start with the work, not the visa keyword
Data scientist postings span experimentation, product analytics, forecasting, NLP, computer vision, risk modeling and machine learning engineering. A search for data scientist sponsorship can mix roles requiring very different evidence.
Choose one or two lanes and search their adjacent titles:
- Product data scientist, experimentation scientist and decision scientist
- Applied scientist, NLP scientist and computer-vision scientist (often with higher degree, research or publication expectations)
- Risk data scientist, fraud scientist and quantitative modeler
- Marketing scientist, customer scientist and causal-inference scientist
- Machine learning scientist and research scientist
Read the methods, data and business outcome. A candidate with experimentation depth should not spend the week applying to production-ML roles merely because they mention sponsorship.
Use posting evidence and history separately
First find the current job's answer. Search its text for sponsor, visa, authorization, immigration, OPT and future. A direct refusal outweighs a strong employer history. Silence means unknown.
Next research the legal employer. USCIS says the US employer petitions for an H-1B worker in a specialty occupation; see the official H-1B program page. The employer normally first files a Labor Condition Application. DOL's LCA program page explains the role, wage and location information in that process.
Look for data scientist, applied scientist and relevant modeling titles in those records. A company with only unrelated filings is a weaker signal than one with repeated data-science activity.
Use data scientist jobs with visa sponsorship for current openings and data scientist H-1B sponsors for the employer view.
Degree and duties should tell the same story
The role's specialized knowledge and your background need a coherent connection. Make that visible without writing legal conclusions on your resume. Put the relevant degree, methods and work together.
For an experimentation role, show hypothesis design, metric definition, power or uncertainty, SQL data construction and the decision that followed. For an applied-ML role, show validation, leakage controls, deployment constraints and monitoring. Built a model with 92% accuracy is incomplete without the baseline, class balance and business cost of error.
OPT candidates have two conversations
The first is whether you are authorized for the role now. The second is whether the employer may support a petition later. The USCIS STEM OPT guidance says post-completion OPT can provide up to 12 months and the STEM extension can add 24 months for qualifying students. STEM OPT also requires an E-Verify employer and at least 20 hours a week.
E-Verify is not an H-1B promise. Give the recruiter dates:
I am authorized under STEM OPT through [month/year]. Would this data scientist role consider a candidate who will need H-1B sponsorship after that date?
Confirm degree eligibility and employment details with your DSO. The recruiter sponsorship guide covers uncertain timelines and application wording.
Avoid three common traps
Applying to every sponsor: a prestigious employer cannot compensate for a poor role match.
Reading an LCA as a job offer: an LCA is a public record in a process, not proof that a vacancy exists or that a petition was approved for a particular candidate.
Ignoring negative language: a referral does not erase no current or future sponsorship in the requisition.
Build a small evidence-rich pipeline
Track employer legal name, role URL, first-seen date, posting verdict, related filing history, recruiter answer and status. Rank roles that say sponsorship is considered, then silent roles at employers with relevant history. Recheck the posting before each interview stage.
Tailor one case study to the role's dominant problem. A dozen focused applications with clear proof are more defensible than a hundred generic ones.
