Jobs / United States / Abbvie INC
Senior Scientist, Informatics II
Abbvie INC · 🇺🇸 Worcester, MA, US
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 177 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 94 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 Abbvie INC paid sponsored hires in similar roles1 certified filing for “Senior Scientist II, Biologics Analytical R&D” (Biochemists and Biophysicists) in MA: $155k–$155k, median $155k. Most were filed at wage level III (100%) — 3 lottery entries, ≈46% 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.
Or apply yourself on the official page →
Sponsor Radar — Abbvie INC
The US Department of Labor certified 177 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 94 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 Abbvie INC →
About the role
The AbbVie Immunology Discovery Pathology group is at the forefront of AI-driven digital pathology, developing quantitative and translational pathology approaches to support target validation, deepen our understanding of disease biology, and advance drug discovery. We are seeking an experienced and innovative Image Analysis Scientist with expertise spanning digital pathology, computer vision, machine learning, and scientific programming, combined with a strong foundation in immunology and tissue biology. Our group generates quantitative insights from H&E and immunolabeled tissue sections and applies emerging spatial transcriptomics and high-plex spatial proteomics technologies to investigate cellular phenotypes, tissue organization, and spatial relationships within the tissue microenvironment. The successful candidate will independently lead the development and application of advanced computer vision and machine-learning approaches for histopathology image analysis, including deep learning and representation learning. The candidate will also develop robust computational workflows for high-dimensional image-based spatial protein datasets, including approaches to identify and quantify cell populations, characterize cellular phenotypes, and analyze spatial relationships and tissue organization. This individual will be part of a highly collaborative, multidisciplinary team and will work closely with pathologists, computational biologists, immunologists, and discovery scientists across the organization. The successful candidate will combine computational innovation with biological insight to develop and apply digital pathology approaches that address important scientific questions and help drive innovative drug discovery research. Responsibilities • Independently design, develop, optimize, and validate advanced machine learning, deep learning, and computer vision approaches for digital pathology, including cell and tissue segmentation, classification, feature extraction, and representation learning, to identify and quantify histopathologic features, cellular phenotypes, and spatial relationships. • Develop and deploy robust image analysis workflows for high-dimensional, multiplexed imaging datasets to quantify cell populations and phenotypes and characterize spatial relationships and tissue organization. • Apply appropriate statistical methods and rigorous model validation strategies to assess model performance and generalizability, including held-out test datasets and quantitative performance metrics such as IoU and F1 score. • Develop computational workflows to integrate and analyze data across multiple imaging and spatial modalities , including H&E, multiplex immunofluorescence, spatial proteomics, and spatial transcriptomics. • Develop Python-based tools and scripts to automate image and data-processing workflows and address interoperability challenges across imaging platforms, analysis tools, and data formats. • Partner closely with pathologists and discovery scientists to perform rigorous quality control, interpret image analysis outputs, and ensure that quantitative results are biologically meaningful. • Apply strong analytical and problem-solving skills to troubleshoot complex image and data analysis challenges and deliver high-quality results within established timelines. • Serve as a subject matter expert in computational digital pathology , providing scientific and technical guidance on image analysis strategies, methodologies, and applications across multidisciplinary project teams. • Stay current with emerging advances in AI, computer vision, and computational pathology, and evaluate and implement new methodologies that can enhance pathology-driven drug discovery. • Communicate analytical strategies , methodologi