2027 Future Talent Program - Patient Data Learning - Co-op
- Pay
- $39,908 – $111,111 per year
- Location
- USA - Massachusetts - Cambridge (320 Bent Str…
- Listed
- 3 days ago
About the role
The Future Talent Program within the Data, AI, and Genome Sciences (DAGS) Department is offering co-op opportunities that last a period of six months. There is one position in the Patient Data Learning group. Candidates can apply for the opportunity to be involved in a computational project designed to enable students to further develop their skills and to evaluate if we are the right company for their long-term goals. We are seeking highly motivated and talented students with computational skills and experience.
Patient Data Learning in Precision Genetics
- Design and build agent-assisted system or components to support feasibility assessment and early exploration of phenotypes and clinical profiles of patients in EHR-linked biobanks.
- Re-use and customize AI language models to turn scientific questions expressed in natural language into machine-generated queries to be evaluated in agentic loops.
- Create guardrails and evals to promote and measure the reliability of models and agents.
- Assist with the application of these tools and technologies to answer feasibility questions.
Required Education and Skills
- Currently enrolled in a minimum of a Master's degree program in Clinical Informatics, Epidemiology, Computational Medicine, Biomedical Informatics, Biostatistics, Computer Science with strong interest in biomedical applications, or a related field of study. Students enrolled in a PhD program are highly encouraged to apply.
- Computational/software engineering fundamentals sufficient to be able run and tune local LLMs, build local agentic systems, and comfortable with applying or outlining software and agent development practices (version control, packaging, tests & evals).
- Proficiency in Python, SQL, ideally database-agnostic querying such as ibis
- Applied knowledge sufficient to query structured EMR/EHR data, for example to create patient features, build cohorts, or report summary statistics, or a demonstrated interest in learning to do so and clinical applications.
- Available to work full-time for 6 months in 2027-2028 as part of a formal co-op program.
- Excellent academic achievement and analytical ability.
- Demonstrated superior communication and interpersonal skills and must be able to work well in a collaborative team environment.
Preferred Skills and Experience
- General interests/experience in ANY of the fields of clinical informatics, clinical machine learning, computational medicine, natural language processing and LLM research, epidemiology
- Good data systems fundamentals, and have at least notions of data engineering and database-agnostic design.
- Agent reward systems
- Knowledge in statistical methods for epidemiology or econometrics.
- Familiarity for the OMOP CDM and medical coding system (ICD, SNOMED, CPT, LOINC, etc.)
- Experience with Databricks.
- Knowledge of R and tidyverse packages.
Additional information
Please note that this position may be closed before the posted end date or may remain open longer, at the discretion of the company.
Salary range
- The salary range for this role is $39,908 - $111,111
- FTP2027
- RL2027
- SWE
Required Skills
Biomedical Applications, Biomedical Informatics, Biomedical Sciences, Clinical Information Systems, Clinical Research, Data Analysis, Database Management, Data Engineering, Data Science, Data Security, Data Visualization, Econometrics, Epidemiologic Methods, Epidemiology, Medical Informatics, Natural Language Processing (NLP), Python (Programming Language), Reliability Analysis, R Package, Software Proficiency, Statistical Methods, Tidyverse (R Package)
Preferred Skills
- Current Employees apply HERE
- Current Contingent Workers apply HERE
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- Employee Status: Intern/Co-op (Fixed Term)
- Relocation: No relocation
- VISA Sponsorship: No
Travel Requirements
- No Travel Required
- Flexible Work Arrangements: Not Applicable
- Shift: 1st - Day
- Valid Driving License: No
- Hazardous Material(s): NA
- Job Posting End Date: 12/4/2026
Additional information
*A job posting is effective until 11:59:59PM on the day BEFORE the listed job posting end date. Please ensure you apply to a job posting no later than the day BEFORE the job posting end date.
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As an Equal Employment Opportunity Employer, we provide equal opportunities to all employees and applicants for employment and prohibit discrimination on the basis of race, color, age, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability status, or other applicable legally protected characteristics. As a federal contractor, we comply with all affirmative action requirements for protected veterans and individuals with disabilities. For more information about personal rights under the U.S. Equal Opportunity Employment laws, visit:
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From MSD's careers page, first listed on Rolev.ai on November 6, 2026. Rolev.ai is not the employer.
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Common questions
What is the salary for the 2027 Future Talent Program - Patient Data Learning - Co-op role at MSD?
The 2027 Future Talent Program - Patient Data Learning - Co-op posting at MSD states compensation of $39,908 – $111,111 per year. Final pay may depend on location, experience, and interview evaluation on MSD's official career site.
Where is the 2027 Future Talent Program - Patient Data Learning - Co-op job at MSD located?
This 2027 Future Talent Program - Patient Data Learning - Co-op position at MSD is located in USA - Massachusetts - Cambridge (320 Bent Str…. Always check MSD's application page for specific state eligibility or relocation policies.
How do I apply for the 2027 Future Talent Program - Patient Data Learning - Co-op position at MSD?
This listing links directly to MSD's Workday application page. You can apply directly on MSD's site, or use Rolev.ai to check your resume fit, generate a zero-hallucination tailored resume and cover letter for this posting, and autofill the Workday application form in 1 click with the Rolev.ai Chrome extension. You always review the form and press Submit yourself.