2027 Future Talent Program - Patient Data Learning - Co-op

Job Description

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 (1 position, Cambridge MA):

• 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.

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

US and Puerto Rico Residents Only:

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We are proud to be a company that embraces the value of bringing together, talented, and committed people with diverse experiences, perspectives, skills and backgrounds. The fastest way to breakthrough innovation is when people with diverse ideas, broad experiences, backgrounds, and skills come together in an inclusive environment. We encourage our colleagues to respectfully challenge one another’s thinking and approach problems collectively.

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