Jobs / United States / Gilead Sciences INC
Sr Analyst, Data Scientist
Gilead Sciences INC · 🇺🇸 United States - California - Foster City
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 159 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 75 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 Gilead Sciences INC paid sponsored hires in similar roles1 certified filing for “Associate Director, Data Scientist” (Data Scientists) in AZ: $195k–$195k, median $195k. 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 — Gilead Sciences INC
The US Department of Labor certified 159 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 75 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 Gilead Sciences INC →
About the role
At Gilead, we’re creating a healthier world for all people. For more than 35 years, we’ve tackled diseases such as HIV, viral hepatitis, COVID-19 and cancer – working relentlessly to develop therapies that help improve lives and to ensure access to these therapies across the globe. We continue to fight against the world’s biggest health challenges, and our mission requires collaboration, determination and a relentless drive to make a difference. Every member of Gilead’s team plays a critical role in the discovery and development of life-changing scientific innovations. Our employees are our greatest asset as we work to achieve our bold ambitions, and we’re looking for the next wave of passionate and ambitious people ready to make a direct impact. We believe every employee deserves a great leader. People Leaders are the cornerstone to the employee experience at Gilead and Kite. As a people leader now or in the future, you are the key driver in evolving our culture and creating an environment where every employee feels included, developed and empowered to fulfil their aspirations. Join Gilead and help create possible, together. Job Description We are seeking a talented and highly motivated Data Scientist for our Advanced Analytical Technologies team within the Analytical Development organization. The Data Scientist will research, develop, and operationalize advanced machine learning and statistical solutions that improve analytical development across Pharmaceutical Development and Manufacturing. This role will translate scientific and business challenges into scalable, data-driven applications spanning quantitative analytics, predictive modeling, computer vision, and other advanced computational methods. The individual will collaborate with lab scientists, data scientists, engineers, and project managers to deliver robust solutions, promote machine learning best practices, and strengthen a culture of data-driven decision-making. Key Responsibilities: • Research and develop machine learning algorithms across quantitative analytics, computer vision, predictive analytics, and advanced statistical modeling to improve pharmaceutical development and manufacturing processes. • Manage the full machine learning model lifecycle , including requirements gathering, exploratory data analysis, visualization, model development, validation, deployment, monitoring, and continuous improvement. • Support end-to-end algorithm and application development , including software package development, compute environment configuration, model architecture, code reviews, testing, documentation, and operationalization. • Translate scientific and business challenges into practical data science solutions with measurable technical, scientific, and operational outcomes. • Apply best practices in machine learning, software engineering, reproducibility, and model governance to ensure deliverables are reliable, scalable, maintainable, and high quality. • Collaborate with multidisciplinary technical teams , including data science architects, data engineers, analysts, project managers, laboratory scientists, and application developers. • Partner with stakeholders in an agile environment to scope, prioritize, plan, design, and execute artificial intelligence and machine learning projects. • Work cross-functionally with analytical development, manufacturing, IT, and technical development teams to advance AI-driven applications and improve scientific and business outcomes. • Promote a data-driven culture by encouraging quantitative decision-making, supporting well-designed experiments, identifying organizational capabilities and needs, and advancing responsible AI adoption. • Communicate complex analyses and model results through clear reports, visualiza