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Image Data Scientist Jobs in Boston, MA (NOW HIRING)

Xometry is seeking an exceptional Principal Data Scientist to join our Generative AI team. The ... Lead the exploration and development of innovative text and image-based data processing solutions ...

Xometry is seeking an exceptional Principal Data Scientist to join our Generative AI team. The ... Lead the exploration and development of innovative text and image-based data processing solutions ...

Clinical Data Analyst II

Somerville, MA · On-site

$94K - $115K/yr

... image databases and tracking systems. * Support data analysis and contributes to interpretation of study results * Develops study documentation, technical reports, and scientific materials ...

... image databases and tracking systems. * Support data analysis and contributes to interpretation of study results * Develops study documentation, technical reports, and scientific materials ...

Clinical Data Analyst II

Somerville, MA · On-site

$94K - $115K/yr

... image databases and tracking systems. * Support data analysis and contributes to interpretation of study results * Develops study documentation, technical reports, and scientific materials ...

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Image Data Scientist information

See Boston, MA salary details

$40.7K

$133.3K

$213.5K

How much do image data scientist jobs pay per year?

As of Sep 5, 2026, the average yearly pay for image data scientist in Boston, MA is $133,343.00, according to ZipRecruiter salary data. Most workers in this role earn between $107,000.00 and $147,800.00 per year, depending on experience, location, and employer.

What is an image data scientist?

Image Data Scientists are professionals who specialize in analyzing, processing, and interpreting image data using advanced techniques such as machine learning, computer vision, and statistical analysis. They work with large datasets of images to extract meaningful insights, develop algorithms for image recognition or classification, and help solve real-world problems in fields like healthcare, autonomous vehicles, and e-commerce. Their role often involves cleaning and labeling image data, building predictive models, and collaborating with software engineers and domain experts to deploy solutions.

What are some common challenges faced by image data scientists when working with large-scale image datasets?

Image Data Scientists often encounter challenges related to managing and processing large volumes of image data, such as ensuring data quality, dealing with imbalanced classes, and handling high computational demands. Efficient data labeling and preprocessing are crucial, as poor annotation can impact model accuracy. Additionally, collaborating closely with engineers and domain experts is key to setting up robust data pipelines and validating model outputs in practical applications.

What are the key skills and qualifications needed to thrive as an image data scientist, and why are they important?

To thrive as an Image Data Scientist, you need expertise in computer vision, machine learning, statistics, and a strong background in mathematics or a related field, often supported by an advanced degree. Proficiency with programming languages like Python, deep learning frameworks (such as TensorFlow or PyTorch), and image processing libraries is typically required. Strong analytical thinking, creativity, and effective communication are essential soft skills for interpreting results and collaborating with multidisciplinary teams. These skills enable the development of accurate image analysis solutions, drive innovation, and ensure the successful implementation of models in real-world applications.

What is the difference between Image Data Scientist vs Computer Vision Engineer?

AspectImage Data ScientistComputer Vision Engineer
Required CredentialsBachelor's/Master's in Data Science, Computer Science, or related fields; knowledge of machine learning and image processingBachelor's/Master's in Computer Science, Electrical Engineering, or related fields; expertise in computer vision algorithms and software development
Work EnvironmentData analysis teams, research labs, AI-focused companiesSoftware development teams, robotics, autonomous vehicles, tech companies
Employer & Industry UsageTech firms, healthcare, retail, research institutionsAutonomous vehicles, robotics, surveillance, augmented reality

While both roles involve working with images and machine learning, an Image Data Scientist primarily focuses on analyzing and interpreting image data to extract insights, often working with large datasets and statistical models. In contrast, a Computer Vision Engineer develops algorithms and software to enable machines to interpret visual information, often involving real-time processing and deployment in applications like autonomous vehicles or AR systems.

What are popular job titles related to Image Data Scientist jobs in Boston, MA?

For Image Data Scientist jobs in Boston, MA, the most frequently searched job titles are:

What job categories do people searching Image Data Scientist jobs in Boston, MA look for?

The top searched job categories for Image Data Scientist jobs in Boston, MA are:

What cities near Boston, MA are hiring for Image Data Scientist jobs?

Cities near Boston, MA with the most Image Data Scientist job openings:

Infographic showing various Image Data Scientist job openings in Boston, MA as of August 2026, with employment types broken down into 1% As Needed, 83% Full Time, 12% Part Time, and 4% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $133,343 per year, or $64.1 per hour.

Data Scientist, AI for Biomedical Imaging

Novartis Group Companies

Cambridge, MA • On-site

Other

Medical, Life, Retirement, PTO

Re-posted 25 days ago


Key responsibilities

  • Develop, validate, and deploy robust image analysis algorithms to characterize cellular, organoid, tissue, and other complex biological phenotypes in high-throughput and high-content imaging data.

  • Lead AI-enabled image analysis strategies for complex biological imaging workflows and act as the scientific partner working with wet-lab scientists to understand assay needs and explain AI concepts.

  • Drive adoption of advanced AI methods for imaging, translating state-of-the-art techniques into practical workflows that support decision-making in drug discovery projects.


Job description

Job Description Summary

The mission of Novartis is to reimagine medicine, and our team advances that mission by applying advanced image analysis, computer vision, and AI methods to early drug discovery. We partner closely with experimental scientists, disease-area teams, data scientists, bioinformatics experts, and platform engineers to extract meaningful biological insight across diverse imaging modalities (high-content screening, custom microscopy platforms) and biological model systems (cellular assays, co-cultures, organoids, tissue models).
To grow this capability, we are seeking a seasoned, innovative, and collaborative data scientist with deep expertise in AI-enabled image analysis to join the Data Science team in Discovery Sciences (DSc) at Novartis Biomedical Research, Cambridge, MA. This role combines hands-on delivery of robust image analysis workflows with advanced AI method development, including biomedical image segmentation, representation learning, foundation models, and scalable deployment. The successful candidate will embed within the research community as the team's scientific lead for imaging-AI, partnering directly with wet-lab scientists to translate complex biological questions into rigorous, reproducible, and impactful analysis strategies.


Job Description

Internal Job Title: Senior Expert I/II, Data Science

Position Location: Onsite, Cambridge, MA #LI-Onsite

Role Responsibilities:

  • Lead AI-enabled image analysis strategies for complex biological imaging workflows, acting as the embedded imaging-AI scientific partner working side-by-side with wet-lab scientists to understand emerging assay needs, align approaches with scientific priorities and platform standards,and explain advanced AI concepts in accessible terms.

  • Identify high-impact opportunities where AI can deliver meaningful scientific value and define rigorous benchmarking and evaluation strategies to guide method selection.

  • Develop, validate, and deploy robust image analysis algorithms to characterize cellular, organoid, tissue, and other complex biological phenotypes in high-throughput and high-content imaging data, generating reproducible outputs that support decision-making in drug discovery projects.

  • Drive adoption of advanced AI methods for imaging, including deep learning, vision foundation models, embedding-based phenotyping, segmentation, classification, and multimodal integration, translating state-of-the-art methods into practical, validated workflows that augment expert review and enable scalable interpretation of large, high-dimensional datasets.

  • Contribute to scalable, reusable image analysis workflows in partnership with other data scientists, data engineering, and platform teams, championing best practices across the workflow lifecycle.

Essential Requirements:

  • PhD in computer science, AI, machine learning, biomedical image analysis, computational imaging, data science, or a related quantitative field, with 3+ years of applied experience in AI for bioimaging and computer vision.

  • Demonstrated experience developing and validating image analysis algorithms for biological, biomedical, or pharmaceutical research applications, with practical experience in image segmentation, feature extraction, phenotypic profiling, object classification, or representation learning applied to high-content or high-throughput imaging data.

  • Practical expertise in designing benchmarking and evaluation strategies to compare image analysis methods and guide rigorous, evidence-based model selection.

  • Ability to work effectively in Linux-based high-performance computing, cloud, or large-scale data processing environments, with a strong commitment to reproducible research, version control, testing, and data provenance.

  • Strong proficiency in Python and the scientific deep learning stack (e.g., PyTorch, Hugging Face, Lightning, MONAI), along with hands-on experience using image analysis tools such as scikit-image, OpenCV, napari, Cellpose, StarDist, InstanSeg, and OME-Zarr.

  • Self-motivated experienced contributor who thrives in a collaborative, multidisciplinary environment with biologists, imaging scientists, software engineers, and bioinformatics partners, working with appropriate independence and helping shape project direction through both technical expertise and scientific judgment.

  • Excellent scientific communication and stakeholder engagement skills, including the ability to explain complex AI and image analysis concepts to experimental scientists, project teams, engineers, and non-technical audiences.

Desirable Requirements:

  • Experience developing or adapting foundation models, self-supervised learning approaches, multimodal AI models, or embedding-based analysis methods (e.g., DINO, CLIP, SAM) for biological imaging data.

  • Familiarity with the drug discovery pipeline, phenotypic screening, translational biology models, or pharmaceutical research processes.

  • Demonstrated success in turning project-specific solutions into reusable, scalable workflows or standardized analysis products integrated into enterprise platforms, production pipelines, or user-facing tools.

  • Track record of scientific publication, conference presentations, open-source contributions, or internal technical leadership in AI, computer vision, biomedical image analysis, or related fields.

  • Familiarity with agentic coding tools and AI-assisted development workflows (e.g., Claude Code, Copilot).

Compensation & Benefits:

The salary for this position is expected to range between $126,000 and $234,000 USD annually for Senior Expert I, Data Science, and $138,600 and $257,400 USD annually for Senior Expert II, Data Science. The final salary offered is determined based on factors like, but not limited to, relevant skills andexperience, and upon joining Novartis will be reviewed periodically. Novartis may change the publishedsalary range based on company and market factors.


Your compensation will include a performance-based cash incentive and, depending on the level of therole, eligibility to be considered for annual equity awards.


US-based eligible employees will receive a comprehensive benefits package that includes health, life anddisability benefits, a 401(k) with company contribution and match, and a variety of other benefits. Inaddition, employees are eligible for a generous time off package including vacation, personal days,holidays and other leaves.


To learn more about the culture, rewards and benefits we offer our people click here.


EEO Statement:

The Novartis Group of Companies are Equal Opportunity Employers. We do not discriminate in recruitment, hiring, training, promotion or other employment practices for reasons of race, color, religion, sex, national origin, age, sexual orientation, gender identity or expression, marital or veteran status, disability, or any other legally protected status.


Accessibility and reasonable accommodations

The Novartis Group of Companies are committed to working with and providing reasonable accommodation to individuals with disabilities. If, because of a medical condition or disability, you need a reasonable accommodation for any part of the application process, or to perform the essential functions of a position, please send an e-mail to us.reasonableaccommodations@novartis.com or call +1(877)395-2339 and let us know the nature of your request and your contact information. Please include the job requisition number in your message.


Salary Range

$126,000.00 - $234,000.00


Skills Desired

Artificial Intelligence (AI), Biostatistics, Change Management, Curious Mindset, Data Governance, Data Literacy, Data Quality, Data Science, Data Visualization, Deep Learning, Graph Algorithms, Learning Agility, Machine Learning (ML), Machine Learning Algorithms, Python (Programming Language), Stakeholder Engagement, Statistical Analysis, Time Series Analysis