Skills this job asks for
LLMsComplianceLeadership
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Sponsorship verdict
Sponsorship possible
One solid signal, not two. Worth applying, and worth asking about sponsorship early.
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✓
Employer is on a government sponsor recordThe US Department of Labor certified 99 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.
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The posting doesn’t mention sponsorshipSilence isn’t a refusal — ask the recruiter before investing much time.
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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.
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✓
What Astrazeneca Pharmaceuticals Lp paid sponsored hires in similar roles4 certified filings for “Director, R&D IT - Dvlpmt & Late TA, IT Product Mgmt Clinic” (Data Scientists) in MD: $194k–$201k, median $194k.
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Confirmed live todayWhen a source last listed this job as open.
Details and sources
- Employer is on a government sponsor record
- The US Department of Labor certified 99 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. Source: LCA disclosure data (US Department of Labor (OFLC)).
- No salary bar for this route
- H-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 Astrazeneca Pharmaceuticals Lp paid sponsored hires in similar roles
- 4 certified filings for “Director, R&D IT - Dvlpmt & Late TA, IT Product Mgmt Clinic” (Data Scientists) in MD: $194k–$201k, median $194k. Most were filed at wage level IV (75%) — 4 lottery entries, ≈61% projected selection odds for cap-subject employers. Source: US Department of Labor LCA disclosure data (Oct 2025 – Jun 2026).
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.
About the role
WHY JOIN US?
Evinova is a health-tech business focused on accelerating better health outcomes by advancing digital transformation across the life sciences sector. By combining science-based expertise, evidence-led rigor, and deep human insight, we design digital solutions that enable healthcare to work better for everyone. Operating at the intersection of healthcare, technology, data, and analytics, we are helping unlock the full potential of digital health, transforming how clinical research is conducted, how care is delivered, and how patients experience healthcare. Our solutions are built to scale, driving efficiency, improving decision-making, and ultimately delivering better outcomes for patients worldwide.
At Evinova, we are driven by a shared purpose to transform health through data and digital innovation. Our teams collaborate across disciplines to solve complex challenges, continuously learning and evolving in a fast-paced, high-impact environment.
We also recognize the importance of flexibility and balance. Our ways of working support both individual needs and team collaboration. To foster connection and collaboration, employees are expected to work from the office three days per week, creating opportunities for in-person teamwork, innovation, and meaningful connection.
Role Summary:
As a Product Data Architect at Evinova, you will shape the data architecture behind our AI-powered products, including Study Designer and AI Document Authoring. You’ll design scalable, secure, and high-quality data solutions that enable smarter, more connected clinical trials.
Working closely with Product and Engineering teams, you’ll define data models, integration patterns, and standards that support both current delivery and future innovation—especially across AI, LLM, and RAG-driven capabilities in a regulated environment.
This is a high-impact role where you’ll help build the data foundations that power the next generation of digital health solutions.
What You’ll Do:
Shape Data Architecture for AI-Powered Products
• Design and evolve data models that power our AI-native products
• Build and guide architectures supporting LLMs, generative AI, and RAG pipelines
• Define scalable data flows, integration patterns, and interfaces across products
• Establish reusable data patterns to support long-term product growth
Ensure Quality, Compliance, and Scalability
• Drive consistency and interoperability across our product ecosystem
• Align data architecture with regulatory and enterprise standards (including GxP)
• Support data governance practices like access, retention, and auditability
• Identify risks and continuously improve data architecture and performance
Use Data to Drive Better Decisions
• Define metrics and dashboards to monitor data quality and system health
• Turn insights into actionable improvements for product and engineering teams
• Promote a strong data quality mindset across teams
Collaborate and Deliver Across Teams
• Partner with Product and Engineering to bring scalable data solutions to life
• Work closely with global teams and external partners to guide implementation
• Stay hands-on when needed—supporting design, troubleshooting, and delivery
Lead and Elevate Data Excellence
• Mentor engineers and help set best practices for data architecture
• Contribute to a shared vision across teams and geographies
• Help define how data supports AI-driven product innovation at scale
What Success Looks Like:
• Data models and interfaces are clear, scalable, and widely adopted across teams
• Product teams move faster with fewer data-related blockers or rework
• Data architecture evolves seamlessly as products, AI capabilities, and usage grow
• LLM and RAG pipelines are reliable, transparent, and
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