Jobs / United States / Abbvie INC
Associate 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.
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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 Artificial Intelligence driven digital pathology and committed to establishing quantitative and translational pathology end points to drive drug discovery research. We seek an innovative and highly motivated research scientist with expertise spanning digital pathology, image analysis, machine learning, artificial intelligence and scientific programming. The successful candidate will develop and deploy image analysis solutions to support quantitative pathology, spatial biology, and multi-modal omics studies. The scientist will work closely with pathologists, biologists, and data scientists to build scalable workflows for the analysis, integration, and management of large imaging datasets, including histology, multiplex immunofluorescence (mIF), spatial transcriptomics, and other emerging imaging technologies. This position is located in Worcester, MA. Responsibilities • Develop, validate, and deploy automated image analysis workflows for histopathology and spatial biology applications, using image analysis platforms (e.g. Visiopharm, Qupath) • Design machine learning and deep learning approaches for cell segmentation, cell phenotyping, biomarker quantification, and spatial analysis. Employ appropriate validation methods for evaluating model performance • Perform image registration, de-arraying, and alignment for multi-modal imaging datasets. • Develop custom scripts, and automated pipelines using Python and related scientific computing libraries. • Configure, monitor, and optimize scalable compute resources for high-throughput image processing. • Architect and integrate multiple databases into a cohesive, scalable system, leveraging a strong understanding of database infrastructure. • Troubleshoot complex software issues and collaborate directly with third-party software engineers to develop novel, tailored solutions • Collaborate with multidisciplinary teams including pathologists, biologists, bioinformaticians, and computational scientists. • Present analytical methods, results, and recommendations to scientific stakeholders. • Maintain accurate documentation of workflows, algorithms, and study results. • Contribute to publications, conference presentations, and scientific innovation initiatives. • Bachelor’s Degree in Computer Science, Data Science, Artificial Intelligence, Biology or related field, or equivalent education, with typically 3 or more years’ experience or Master’s Degree in Computer Science, Data Science, Artificial Intelligence, Biology or related field or equivalent education (no additional experience). • Experience in the pharmaceutical industry is preferred. • Demonstrated experience in digital pathology and image analysis platforms (Visiopharm, Halo, QuPath, Image J), plus a deep understanding of image analysis core concepts, including image processing and machine learning. • Strong proficiency in Python and associated scientific computing libraries (NumPy, Pandas, SciPy, Scikit-learn, or similar). • Working knowledge of cloud computing platforms (AWS), scalable computational workflows and database infrastructure • Proficiency in the use of third-party software tools to support data analysis tasks (e.g., GraphPad Prism, Spotfire, Excel). • Strong problem-solving, communication, documentation and collaboration skills. Desirable • Knowledge of molecular pathology techniques (e.g., immunohistochemistry, immunofluorescence, in situ hybridization etc.). • Experience with spatial transcriptomics images and multiplexed IF images • A foundation in biology or immunology is highly desirable, to support the development and interpretation of image analysis solutions for translational research Applicable only to applicants ap