AI Data Specialist Jobs in Massachusetts
AI Data Specialist jobs in Massachusetts concentrate heavily in life sciences, financial services, and technology, with the state ranking among the most active markets in the country for data-driven roles at every level from entry analyst to senior architect. Boston, Cambridge, and Waltham are the primary hiring hubs, anchored by employers such as Biogen, Fidelity Investments, and MathWorks. The most sought-after specialties in Massachusetts listings include machine learning pipeline development, clinical and genomic data analysis, and NLP model evaluation. Scan the live roles below and apply to whichever ones fit.
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Mass General Brigham relies on a wide range of professionals, including doctors, nurses, business people, tech experts, researchers, and systems analysts to advance our mission. As a not-for-profit, we support patient care, research, teaching, and community service, striving to provide exceptional care. We believe that high-performing teams drive groundbreaking medical discoveries and invite all applicants to join us and experience what it means to be part of Mass General Brigham.
Job Summary
One full-time (40 hours/week) hybrid position is available. When applying, please indicate the track to which you are applying:
- Computational Track (Ph.D. in a STEM discipline)
- Clinician-Scientist Track (M.D., Ph.D, or equivalent clinical degree)
Application Deadline: Applications will be reviewed on a rolling basis beginning October, 2026 and accepted until the position is filled.
Anticipated Start Date: February, 2027 (flexible)
Description
The NEIL AND ELISE WALLACE STRATUS Center for Medical Simulation at BWH is committed to advancing medical education, patient safety, and healthcare outcomes through cutting-edge applications of medical simulation technologies supported by innovative curricula, contemporary educational methodologies, continuous improvement, and rigorous, multidisciplinary research.
At STRATUS, the Medical AI & Cognitive (MAICE) Lab, led by Dr. Roger Dias, MD, PhD, MBA, brings together a multidisciplinary team of clinicians, engineers, psychologists, computer scientists, and educators, including collaborations with several scientists and institutions, nationally and internationally. The core mission of our research lab involves the use of cutting-edge technologies to objectively measure and improve clinicians’ performance, with the ultimate goal of enhancing patient safety and improving clinical outcomes. Our projects have been funded by NASA, NIH, DoD, and NSF, leveraging machine learning and artificial intelligence to support high-performance clinical care in a variety of settings, including emergency medicine, critical care, surgery, space, and military medicine.
The Postdoctoral Fellowship in Medical AI prepares the next generation of researchers to bridge artificial intelligence and clinical care. Building AI that clinicians trust and use requires both technical depth and clinical insight, so the fellowship is open to candidates from two backgrounds who work side by side in interdisciplinary teams:
- Computational Track – for graduates of doctoral programs in Science, Technology, Engineering, and Mathematics (STEM) with a solid foundation in applying advanced ML/AI techniques. This track is ideal for early-career scientists seeking experience in the healthcare field and the opportunity to apply AI/ML and software engineering to improve patient care and safety. Prior experience in healthcare or biomedical research is required.
- Clinician-Scientist Track – for clinicians (M.D., D.O., PhD, or equivalent) with research experience and a strong interest in medical AI. This track is ideal for clinicians who want to lead the design, clinical validation, and implementation of AI tools while building hands-on data science and AI skills. This is a full-time research position with no clinical duties.
Fellows on both tracks are mentored by a multidisciplinary team of experts, gaining extensive knowledge and experience in diverse research areas including medicine, human factors, cognitive science, behavioral sciences, and aerospace and military research. Fellow follow an individualized development plan, with support for first-author publications, presentations at national and international conferences, and applications for independent fellowships and career development awards.
Responsibilities
All fellows will:
- Conduct research and development in the emerging field of medical AI, from study design through publication, in settings such as emergency medicine, critical care, surgery, space, and military medicine
- Work with large multi-source clinical datasets, including time-series physiological data, demographics, behavioral and psychological assessments, video, audio, electronic health records (EHR), and clinical performance outcomes
- Contribute to the development and evaluation of AI-based medical simulation applications and real-time clinical decision support systems
- Lead first-author manuscripts and conference presentations, and contribute to grant proposals
- Collaborate closely with clinicians, engineers, and scientists, and help mentor students and junior team members
- Designing and developing software architectures and data pipelines for AI-based solutions
- Training, fine-tuning, and evaluating ML/AI models, including neural networks, large language models, and computer vision models
- Integrating AI models with large clinical databases and deploying them in simulation and real-time clinical environments
- Identifying clinically meaningful problems and translating them into AI research questions and study designs
- Designing and conducting simulation-based and clinical studies, including writing IRB protocols and leading data collection
- Defining clinical ground truth, including expert annotation of video, audio, physiological, and EHR data
- Evaluating AI systems, including large language models, for clinical accuracy, safety, bias, and usability, and studying how clinicians interact with AI tools
- Building hands-on skills in data analysis, programming, and AI through mentored projects and available training opportunities
Qualifications
Applicants must meet the requirements for all applicants plus those for their chosen track. Preferred qualifications are not required; we encourage you to apply even if you do not meet all of them.
Required for all applicants:
- Doctoral degree (Ph.D., M.D., or equivalent) completed by the start date
- At least three peer-reviewed publications in which the candidate was a significant contributor, including at least one as lead author. Both journal articles and full-length peer-reviewed conference papers (e.g., NeurIPS, ICML, MICCAI, CHI, ML4H) count.
- Strong interest in a research career at the intersection of AI and medicine
- Excellent written and verbal communication skills and the ability to work effectively in multidisciplinary teams
Required for the Computational Track:
- Ph.D. in a STEM discipline (e.g., computer science, electrical or biomedical engineering, data science, statistics)
- Proficiency in Python and/or C++
- Demonstrated experience with AI/ML model training and evaluation, including neural networks and/or large language models
- Demonstrated experience with PyTorch and/or TensorFlow and/or scikit-learn
- Familiarity with signal processing and time series analysis
Preferred for the Computational Track:
- Prior experience applying AI/ML to medical or health data
- Experience with computer vision (e.g., convolutional neural networks) and/or vision-language models
- Familiarity with cloud computing services and APIs
- Experience with multimodal data (e.g., video, audio, and physiological signals) or real-time systems
Required for the Clinician-Scientist Track:
- M.D., D.O., Ph.D., or equivalent clinical degree
- Demonstrated clinical research experience, including study design, data collection, and statistical analysis
- Hands-on experience analyzing research data using R, Python, Stata, SAS, or a similar tool
- Demonstrated engagement with AI, data science, or digital health, such as a research project that used AI/ML methods, an evaluation of AI tools or large language models, or coursework or a certificate in data science, AI, or clinical informatics
Preferred for the Clinician-Scientist Track:
- Completed residency or other clinical training, especially in emergency medicine, critical care, anesthesiology, or surgery
- Advanced training in research methods, epidemiology, biostatistics, or informatics (e.g., MPH, MS, MMSc, or Ph.D.)
- Programming experience in Python, including use of ML libraries
- Experience writing IRB protocols, conducting prospective data collection, or doing healthcare simulation research
Salary and Benefits
The selected candidate will be appointed for two years, with the opportunity to renew the appointment for a third and/or fourth year pending performance review and funding. The salary range for this position is $71,750 to $73,544 annually. Benefits include comprehensive health insurance, optional dental, vision, and retirement plans, and reimbursement for conference travel related to fellowship projects. BWH is a Harvard-affiliated teaching hospital, and the research fellow will be eligible for an appointment at Harvard Medical School.
Additional Job Details (if applicable)
IMPORTANT – APPLICATION INSTRUCTIONS
Please do not apply through the Workday “Apply” button.
This position is being posted for informational purposes only. To be considered, please submit the following directly to the hiring manager by clicking the link below:
- Cover letter (up to two pages) stating your track (Computational or Clinician-Scientist), your research interests and relevant experience, and what you hope to accomplish during the fellowship
- Current CV, including a full list of publications
- Recent work sample (e.g., a first-author peer-reviewed manuscript or pre-print; Computational Track applicants are encouraged to also include GitHub code or a software demo)
- Candidates selected for an interview will be asked to submit two letters of recommendation
pborges1@bwh.harvard.edu
Please note: Applications submitted through Workday will not be forwarded to the hiring manager and may not be reviewed.
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EEO Statement:
Mass General Brigham Competency Framework
At Mass General Brigham, our competency framework defines what effective leadership “looks like” by specifying which behaviors are most critical for successful performance at each job level. The framework is comprised of ten competencies (half People-Focused, half Performance-Focused) and are defined by observable and measurable skills and behaviors that contribute to workplace effectiveness and career success. These competencies are used to evaluate performance, make hiring decisions, identify development needs, mobilize employees across our system, and establish a strong talent pipeline.
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AI Data Specialist Job Market in Massachusetts
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Top Industries Hiring
- Insurance
- Law & Legal Services
- Technology & Software
- Healthcare & Medical Services
- Biotechnology & Pharmaceuticals
What Massachusetts Employers Look For
The qualifications that appear most often in AI data specialist jobs across Massachusetts.
- Bachelor's or master's degree in data science, computer science, or a related quantitative field
- Proficiency in Python or R for data processing, modeling, and automation workflows
- Experience building or fine-tuning machine learning models using frameworks such as TensorFlow or PyTorch
- Familiarity with cloud data platforms including AWS, Azure, or Google Cloud
- Demonstrated ability to work with large structured and unstructured datasets and SQL databases
- Strong written and verbal communication skills for presenting findings to non-technical stakeholders
AI Data Specialist Jobs in Massachusetts: Frequently Asked Questions
How do you become a ai data specialist in Massachusetts?
A bachelor's degree in data science, statistics, computer science, or a related field is the standard entry point for ai data specialist roles in Massachusetts, with many employers in the life sciences and financial sectors preferring a master's degree. There is no state-issued license required to work in this role in Massachusetts. Candidates strengthen their applications through industry certifications in cloud platforms or machine learning, and by building a portfolio of applied projects relevant to Massachusetts's dominant sectors such as biotech, genomics, or quantitative finance.
How much do AI data specialists make in Massachusetts?
AI data specialists in Massachusetts earn a median of about $131,750 a year, based on May 2025 Bureau of Labor Statistics wage data, ranging from around $81,030 for the lowest 10% to over $206,220 for the top 10%. Pay rises with experience, specialty, and employer.
Which companies hire ai data specialists in Massachusetts?
Employers hiring ai data specialists in Massachusetts right now include Brandeis University, Plymouth Rock Assurance, and Novartis, based on current listings on Migrate Mate as of November 2026. Massachusetts's concentration of research hospitals, biotech firms, and financial institutions makes it one of the most consistently active states for this role year-round.
Which Massachusetts cities have the most ai data specialist jobs?
Boston, Waltham, and Cambridge have the most ai data specialist openings in Massachusetts. Boston and Cambridge dominate because of their dense clusters of research universities, teaching hospitals, and technology headquarters, while Waltham draws significant hiring from established software and life sciences companies that have long maintained operations along the Route 128 corridor.
Are there remote ai data specialist jobs in Massachusetts?
Yes, and more than most fields, since the work is almost entirely computer-based and output-driven. About 50% of ai data specialist openings tied to Massachusetts are remote or hybrid as of November 2026, reflecting how well the role adapts to distributed teams. Roles focused on model development, data pipeline engineering, and NLP annotation tend to offer the most remote flexibility, while positions requiring collaboration with wet-lab or clinical teams at Massachusetts research institutions are more likely to require on-site presence.
How can I get hired as a ai data specialist in Massachusetts with little or no experience?
The most realistic entry path is through a data analyst or research associate role at a Massachusetts life sciences or financial services company, then moving into ai data work as internal projects grow. Large Massachusetts employers such as Biogen and Fidelity run new-graduate rotational and associate programs that include data and analytics tracks. Building a public portfolio of machine learning or NLP projects on GitHub, completing a cloud certification, and targeting contract or co-op positions at Boston-area biotech firms can all give candidates without direct experience a credible foot in the door.
Where can I find and apply to ai data specialist jobs in Massachusetts?
You can find and apply to ai data specialist jobs in Massachusetts on Migrate Mate, which lists current Massachusetts openings updated regularly. Search the listings, find the roles that match your background and preferences, and apply directly to the ones that fit.
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