Jobs / United States / Plaid INC
Data Science Manager - Fraud
Plaid INC · 🇺🇸 New York City Office; Seattle Office; San Francisco HQ
Sponsorship verdict
Sponsorship possible
One solid signal, not two — worth applying, and worth asking about sponsorship early.
- Employer is on a government sponsor recordThe US Department of Labor certified 27 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first. USCIS also records 9 H-1B approvals in FY2023. Source: LCA disclosure data (US Department of Labor (OFLC)).
- The posting doesn’t mention sponsorshipSilence isn’t a refusal — ask the recruiter before investing much time.
- No salary bar for this routeH-1B has no fixed salary bar: the employer must pay at least the prevailing wage for the role and area. Cap-subject employers enter a lottery weighted by wage level. Source: https://www.federalregister.gov/documents/2025/12/29/2025-23853/weighted-selection-process-for-registrants-and-petitioners-seeking-to-file-cap-subject-h-1b, rules effective 2026-02-27.
- What Plaid INC paid sponsored hires in similar roles2 certified filings for “Senior Machine Learning Engineer” (Data Scientists) in NY: $170k–$226k, median $198k. Most were filed at wage level IV (100%) — 4 lottery entries, ≈61% projected selection odds for cap-subject employers. Source: US Department of Labor LCA disclosure data (Oct 2025 – Jun 2026).
- Confirmed live todayWhen a source last listed this job as open.
US H-1B: cap-subject employers enter a lottery weighted by wage level — Level I gets 1 entry, Level IV gets 4 (DHS projected selection odds ≈15% at Level I to ≈61% at Level IV). Universities and non-profit research employers are cap-exempt. The $100,000 fee for new petitions from abroad is currently blocked by a court order (appeal pending).
A verdict summarises public evidence; it is not legal advice and never a guarantee — the employer and the immigration authority decide. Sign in to factor in where you can already work.
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Sponsor Radar — Plaid INC
The US Department of Labor certified 27 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first. USCIS also records 9 H-1B approvals in FY2023. Source: LCA disclosure data (US Department of Labor (OFLC)).
Past sponsorship or register membership never guarantees sponsorship for this vacancy or for you. Full Sponsor Radar for Plaid INC →
About the role
We believe that the way people interact with their finances will drastically improve in the next few years. We’re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid’s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Seattle, Washington D.C., Raleigh, London, and Amsterdam. Fraud Data is the data science and machine learning team within Plaid’s Fraud organization, responsible for using data and ML to improve and scale Plaid’s fraud products. Within Fraud Data, the Customer & Product Intelligence team focuses on understanding product performance, uncovering customer insights, and enabling go-to-market teams with data-driven solutions. The team partners closely with customers and GTM teams on fraud analyses and proofs of concept, turning customer learnings into scalable, reusable product capabilities. We also build the metrics, analytics, and data foundations that measure product health, identify opportunities for improvement, and guide product decisions across Plaid’s Fraud portfolio. As a Data Science Manager, you will lead a team responsible for customer-facing data science and Fraud product analytics. You will set the team's roadmap, develop its data scientists, and remain involved in analytical methods, technical reviews, and customer investigations. You will: - Set a 6–12-month roadmap with Product, Engineering, and GTM, and assign priorities and responsibilities across the team. - Define product metrics, their underlying data, and reporting and alerting practices; use the results in roadmap and investment decisions. - Establish a repeatable process for customer retrospectives and proofs of concept, including data checks, evaluation methods, and clear recommendations. - Identify fraud signals and product opportunities that recur across customer analyses and work with Product and MLEs to develop them. - Review analytical designs, data models, code, and model evaluations; contribute directly to investigations where your expertise is needed. - Coach data scientists through clear expectations, regular feedback, performance discussions, and growth opportunities. - Use AI-assisted analysis and development tools where useful, and ensure results are properly reviewed before informing customer recommendations or product decisions. Responsibilities: - Define how Plaid measures, evaluates, and improves the performance of its Fraud products. - Apply fraud expertise, product analytics, and customer-facing data science to drive end-to-end product and business impact. - Translate customer insights and fraud analyses into scalable product capabilities and opportunities for GTM growth. - Lead and develop a high-performing team while remaining technically hands-on with critical analyses and initiatives. - Raise the bar for product metrics, analytical rigor, and the data foundations that power decision-making across Fraud. Qualifications: - Proven experience managing, mentoring, and developing high-performing data scientists. - Deep domain expertise in fraud, risk, or related areas. - Strong experience in product analytics, metric design, and measuring product performance. - Experience partnering directly with customers to deliver data-driven insights and solutions. - Strong technical depth i