Research Associate

Position

Details

Title Research Associate

School Harvard School of Dental Medicine

Department/Area Oral Medicine, Infection, and Immunity

Position Description The Feres Lab, located within the Department of Oral Medicine, Infection, and Immunity at the Harvard School of Dental Medicine (HSDM), is seeking a highly motivated Research Associate with strong data engineering and machine learning skills. The role is well suited to an early career data professional interested in biomedical research or to an experienced data engineer or data scientist looking for a senior technical role in an academic environment. Under the supervision of the principal investigator, the selected candidate will work within a multidisciplinary team of dentists, microbiologists, and data scientists at the Department of Oral Medicine, Infection, and Immunity, Harvard School of Dental Medicine, and the Core for Computational Biomedicine, Department of Biomedical Informatics, Blavatnik Institute, Harvard Medical School. The Research Associate will serve as the technical lead for the team’s research data infrastructure.

Key responsibilities include:

• Designing, building, and maintaining reproducible pipelines that harmonize clinical, demographic, microbiome, and immunological data into a common data model
• Maintaining data dictionaries, version control, and automated data quality checks, with secure data storage and access in compliance with institutional policies
• Implementing, training, and evaluating machine learning models
• Preparing analytic code, models, and documentation for public release
• Contributing to manuscripts, figures, and grant applications

The position offers coauthorship on project publications, access to the Harvard Medical School high-performance computing cluster, and Harvard’s professional development resources.

This is an annual term appointment, reviewed each academic year on or before June 30th, with the possibility of extension depending on work performance, business needs, and continued funding.

Basic Qualifications

The following are basic qualifications for applicants:

• Master’s degree in data science, computer science, data engineering, biostatistics, bioinformatics, health informatics, or a related quantitative field and at least 2 years of relevant experience, or a bachelor’s degree in one of these fields and at least 4 years of relevant experience in data engineering, data science, or research computing.
• A doctoral degree in a related field may substitute for experience.
• Proficiency in Python or R, and in SQL, for the processing and analysis of research data.
• Excellent written and spoken communication in English and the ability to work across clinical and computational teams.

Additional Qualifications

Applicants with the following experience are preferred:

• Building data pipelines (ETL or ELT) and relational databases for research or health data, including the integration of datasets from multiple sources.
• Machine learning applied to clinical or biomedical data, including model validation and evaluation. Experience with Git, containers, and shared high-performance research computing (e.g., SLURM) or cloud environments.
• Experience with microbiome (16S rRNA and/or shotgun metagenomic), immunological assay, or clinical trial data.
• Strong documentation habits and attention to detail. Industry experience is welcome; candidates from technology, health care, or life sciences are encouraged to apply.

Special Instructions Interested candidates should submit a curriculum vitae or résumé and a cover letter describing their technical experience and skills. Links to code repositories (e.g., GitHub) or examples of relevant work may be included in the cover letter. Candidates will be asked to provide contact information for three references.

Contact Information Sophie Spengler Harvard School of Dental Medicine 188 Longwood Avenue Boston, MA 02115

Contact Email omii@hsdm.harvard.edu

Salary Range $60,000-$80,000

Minimum Number of References Required

Maximum Number of References Allowed

Keywords

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