Sr. Principal Scientist, Spatial Omics

At Johnson & Johnson, we believe health is everything. Our strength in healthcare innovation empowers us to build a world where complex diseases are prevented, treated, and cured, where treatments are smarter and less invasive, and solutions are personal. Through our expertise in Innovative Medicine and MedTech, we are uniquely positioned to innovate across the full spectrum of healthcare solutions today to deliver the breakthroughs of tomorrow, and profoundly impact health for humanity. Learn more at jnj.com.

As guided by Our Credo, Johnson & Johnson is responsible to our employees who work with us throughout the world. We provide an inclusive work environment where each person is considered as an individual. At Johnson & Johnson, we respect the diversity and dignity of our employees and recognize their merit.

Job Function: Discovery & Pre-Clinical/Clinical Development

Job Sub Function: Biological Research

Job Category: Scientific/Technology

All Job Posting Locations: Cambridge, Massachusetts, United States of America, Spring House, Pennsylvania, United States of America

Job Description:

About Innovative Medicine: Our expertise in Innovative Medicine is informed and inspired by patients, whose insights fuel our science-based advancements. Visionaries like you work on teams that save lives by developing the medicines of tomorrow. Join us in developing treatments, finding cures, and pioneering the path from lab to life while championing patients every step of the way. Learn more at https://www.jnj.com/innovative-medicine

We are searching for the best talent for a Senior Principal Scientist, Spatial Omics in Cambridge, MA or Spring House, PA.

Purpose:

As a Senior Principal Scientist in Spatial Omics, you will be a high-impact individual contributor and scientific thought leader driving advanced computational innovation across multimodal biological datasets. You will operate at the intersection of machine learning, systems biology, spatial genomics, and computational modeling, delivering analytical breakthroughs that transform how biological complexity is understood and leveraged in therapeutic discovery.

In this role, you will independently design, build, and apply cutting-edge AI/ML frameworks to extract deep insights from spatial omics disease and normal maps combined with orthogonal genomics, transcriptomics, proteomics, metabolomics, and single-cell data. You will develop and deploy ML-based and/or agent-based models (ABM) to simulate cellular, tissue-level, and microenvironmental dynamics, enabling mechanistic predictions and hypothesis generation that augment experimental biology. Your work will bridge predictive, generative, and mechanistic modeling, creating a unified computational layer that drives discovery across therapeutic areas.

Beyond algorithm selection, deployment, and development, you will play a mission-critical role in shaping the Multi-omics computational ecosystem. You will map, influence, and guide the evolution of the data and modeling architecture, collaborating closely with data engineering, platform, and scientific partners to ensure that infrastructure, pipelines, and data standards are optimized for next-generation omics, high-dimensional analytics, and large-scale ML training. Your architectural guidance will enable scalable, reproducible, cloud-native workflows that support both routine and exploratory science.

As a recognized expert in the field, you will exert scientific leadership through influence rather than direct personnel management; advising teams, championing best practices, shaping strategic priorities, and representing computational innovation internally and externally. Your contributions will accelerate target discovery, deepen mechanistic understanding, refine patient stratification, and guide biomarker development, ultimately shaping portfolio decisions and scientific strategy across the organization.

This role is ideal for a scientist who thrives on scientific depth, architectural thinking, cross-disciplinary problem-solving, and the intellectual independence to push the boundaries of what is computationally and biologically possible.

Responsibilities:

• Advanced Computational Omics Research:
• Develop and apply state-of-the-art AI/ML, statistical, and computational frameworks to analyze genomics, transcriptomics, proteomics, metabolomics, single-cell, and multi-omics datasets.
• Lead the design and execution of spatial omics analyses at massive scale, integrating imaging-based, sequencing-based, and multiplexed spatial platforms to uncover tissue architecture, cellular neighborhoods, and microenvironmental dynamics.
• Build scalable pipelines to preprocess, QC, harmonize, and integrate terabyte- to petabyte-scale spatial omics datasets, enabling discovery-ready data layers and advanced modeling.
• Mechanistic & Predictive Modeling:
• Deploy, adapt, and develop agent-based models (ABM) to simulate cellular interactions, tissue-level organization, and dynamic biological processes, incorporating outputs from multimodal omics and spatial measurements.
• Fuse mechanistic models with ML/AI frameworks to generate hybrid predictive systems for target discovery, perturbation response, and disease progression modeling.
• Algorithm & Platform Innovation:
• Deploy and create novel ML architectures, including deep learning, generative models, graph neural networks, and causal inference frameworks that are tailored for biological complexity.
• Design and implement scalable algorithms for high-dimensional, multimodal integration of spatial, molecular, and phenotypic data.
• Prototype and benchmark cutting-edge computational approaches, pushing the frontier of in silico biological inference.
• Data Architecture & Infrastructure Leadership:
• Map, influence, and guide the development of computational and data architecture needed to support next-generation omics and ML workloads.
• Partner with data engineering and platform teams to define standards for data ingestion, modeling workflows, metadata management, and reproducible research ecosystems.
• Ensure infrastructure supports large-scale distributed training, complex spatial analytics, cloud-native computation, and long-term model governance.
• Cross-Functional Scientific Leadership (IC Role):
• Act as a senior scientific authority, shaping strategy and guiding decision-making across discovery and platform innovation, without direct people management.
• Provide high-level technical mentorship, scientific critique, and modeling guidance to colleagues and collaborators.
• Drive cross-disciplinary project teams by defining computational strategy, interpreting results, and ensuring scientific rigor.
• Impact & Translation:
• Deliver insights that advance target identification, mechanism-of-action exploration, pathway modeling, biomarker discovery, and patient stratification.
• Translate computational discoveries into actionable biological hypotheses, experimental designs, and portfolio-impacting recommendations.
• Communicate findings effectively to scientific and strategic stakeholders.

Qualifications/Requirements:

• Education: Minimum of a Ph.D. in Computational Biology, Bioinformatics, Computer Science, Statistical Genetics, Systems Biology, Applied Mathematics/Physics, or a related quantitative discipline.
• Skills/Experience:
• Minimum of 9 years of post-doctoral, industry or academic experience applying advanced computational, statistical, and machine-learning methods to biological problems.
• Deep expertise across multiple omics modalities, including genomics, transcriptomics, proteomics, metabolomics, and spatial omics (e.g., spatial transcriptomics, multiplexed imaging, spatial proteomics).
• Demonstrated ability to analyze, integrate, and interpret very large-scale, multimodal datasets (multi-TB to PB scale), including the design of scalable pipelines and distributed computation strategies.
• Expert-level proficiency in modern ML/AI frameworks, such as PyTorch, TensorFlow, JAX, scikit-learn, and deep-learning architectures relevant to biological modeling.
• Strong background in agent-based modeling, systems biology modeling, or hybrid mechanistic-ML modeling frameworks.
• Proven ability to design and influence data and computational architectures, including experience working with cloud-native analytical ecosystems (Azure, AWS, or GCP) and large-scale data engineering workflows.
• Demonstrated scientific leadership as an individual contributor, including the ability to independently drive complex research programs, set technical direction, and influence cross-functional strategy.
• Strong publication record in high-impact journals or top-tier ML/AI conferences, reflecting innovation in computational biology or applied machine learning.
• Proficiency in Python and experience with scientific computing libraries (NumPy, SciPy, pandas) and workflow orchestration tools.

Expected Pay Range: $137,000 to $235,750

Company Benefits:

• Competitive salary
• Performance-based bonus
• Comprehensive medical, dental, and vision insurance
• Life insurance and disability insurance
• 401(k) retirement plan
• Paid time off, including vacation, sick leave, and holidays
• Parental leave and caregiver leave
• Volunteer leave and military spouse time-off
• Employee assistance program
• Tuition reimbursement and education assistance
• Stock options and equity participation

Job Posting Closure: March 7, 2026

Johnson & Johnson is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, age, national origin, disability, protected veteran status or other characteristics protected by federal, state or local law. We actively seek qualified candidates who are protected veterans and individuals with disabilities as defined under VEVRAA and Section 503 of the Rehabilitation Act.