Jobs / United States / Novartis Pharmaceuticals Corporation
Bioinformatics / Omics Data Expert
Novartis Pharmaceuticals Corporation · 🇺🇸 Cambridge (USA)
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 75 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 31 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 Novartis Pharmaceuticals Corporation paid sponsored hires in similar roles1 certified filing for “Expert Science & Technology” (Biochemists and Biophysicists) in MA: $122k–$122k, median $122k. 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).
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Sponsor Radar — Novartis Pharmaceuticals Corporation
The US Department of Labor certified 75 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 31 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 Novartis Pharmaceuticals Corporation →
About the role
Job Description Summary #LI-Hybrid Location: Cambridge, MA Job Description Summary The Oncology Data Science (OncDS) team in Biomedical Research provides computational biology, AI, and data expertise and brings together projects spanning the entire pre-clinical to clinical development pipeline across a wide and unique range of novel therapeutics. OncDS specializes in using high-throughput genomic and biomarker data for target identification, drug discovery, and clinical development. We are seeking a motivated and curious Scientific Data Engineer at the beginning of their professional data journey to support the development, maintenance, and modernization of FAIR oncology reference datasets. This role combines hands-on data curation with workflow automation, AI-enabled process improvement, dataset refresh and quality control, and fit-for-purpose data engineering to help ensure that key oncology data assets are reliable, reproducible, and ready for downstream scientific use. This position will work closely with senior team members and cross-functional collaborators to modernize data curation processes, identify opportunities for AI-enabled automation, and contribute to high-value reference datasets that support Oncology-wide data strategy and FAIR data goals. The role is designed to build practical experience in scalable data practices, operational excellence, and automation-driven continuous improvement. Why join us: This is an opportunity to work at the intersection of data engineering, computational biology, and oncology research, contributing to data assets that enable important scientific decisions across the drug discovery pipeline. You will join a collaborative team that values curiosity, learning, and continuous improvement, and you will have the chance to grow your technical and scientific skills while helping build reliable, scalable data foundations for cutting-edge oncology research. Job Description Key responsibilities: • Work with senior team members to identify opportunities for simplification, automation, and continuous improvement in operational data processes, including modernization of data curation workflows • Support the development and improvement of automated workflows for data processing, quality control, recurring dataset updates, and AI-enabled process improvement • Collaborate with cross-functional teams to curate, maintain, and improve key oncology reference datasets, with an emphasis on quality, correctness, reproducibility, and operational reliability • Contribute to reference dataset lifecycle management, including documentation, versioning, and traceable update processes • Assess dataset structure, integrity, and fitness for downstream scientific use, including attention to identifiers, primary keys, missing values, consistency, and related data quality issues, and help resolve data issues in collaboration with relevant partners • Contribute to best practices for reproducible workflows, data handling, and scalable dataset operations • Help keep oncology reference datasets current, usable, and responsive to evolving scientific and organizational priorities Essential Requirements: • Degree in bioinformatics, computational biology, data science, computer science, or a related field, or equivalent relevant experience • Experience working with omics datasets in a research environment • Proficiency in one or more programming languages used in data science or bioinformatics, such as Python or R • Experience with data wrangling, data quality control, and reproducible analysis workflows • Familiarity with workflow automation and scripting for recurring data processing tasks • Familiarity with database principles and sound data modeling practices, including normalization, primary keys, join