Merck · Summer 2027

Global Lung Medical Affairs Intern: a project built for this posting

This intern helps Merck's global lung cancer medical affairs group plan and run its presence at major oncology congresses such as ASCO and ESMO, including tracking competitor and internal data readouts. They also use AI tools to mine scientific insights for sentiment themes and draft a framework for how such tools should be applied to insight analysis.

Company: Merck
Posting title: 2027 Future Talent Program - Global Lung Medical Affairs - Intern
Location: Rahway, NJ
Season: Summer 2027
Pay (as posted): $39,908 - $111,111 USD (salary range)
Deadline (as posted): 2026-11-14
Majors named: Life sciences, Pharmacy, Science/medical fields (BS/BA, MS, PharmD/PhD students)

Checked open on the employer's careers site on 2026-10-16. Read the full posting ↗

What this role is really testing

Scientific insights synthesis and analysisAI prompt engineering for qualitative dataSentiment theme identification and semi-quantificationTranslating findings into actionable recommendationsMedical congress landscape awarenessStructured methodology documentationCross-functional communication of complex data

The project

AI-Assisted Scientific Insights Analysis: Lung Cancer Congress Themes

A structured, reproducible methodology document and annotated findings report that uses a publicly available AI tool to analyze real abstracts or press releases from a recent lung cancer congress (e.g., WCLC or ESMO) to identify, semi-quantify, and visualize sentiment themes around a priority topic such as a drug class or biomarker. This maps directly to the role's core ask: building an AI-driven insights methodology framework and translating findings into actionable medical affairs recommendations.

Collect 10–15 publicly available congress abstracts or published summaries from a recent WCLC, ESMO, or ASCO lung cancer meeting focused on one topic (e.g., KRAS G12C inhibitors or PD-L1 expression). Paste them into a single document that will serve as your raw data set.
Write and document a repeatable prompt sequence in ChatGPT or a comparable AI tool: first a prompt to extract key sentiment themes per abstract (positive efficacy signal, safety concern, unmet need, competitive positioning), then a second prompt to count theme frequency across all abstracts and assign a relative weight (high/medium/low) to each. Record both prompts and the raw outputs in a clearly labeled results table, this table is the semi-quantification deliverable.
Compile a clean, two-page PDF report containing: (1) a one-paragraph methodology framework explaining your prompt design logic and how it could scale to larger insight sets, (2) a simple bar or table visualization of the ranked sentiment themes, and (3) three bullet-point actionable recommendations a Medical Affairs Lung team could act on based on the findings, for example, where a competitor signal is strengthening or where a data gap represents an educational opportunity.

Build this project for free →

Free account. Provieo walks you through it and turns it into a page with a link you can put on your application. This posting comes with you.

Questions

Do I need to know how to code?

No. For roles like this one, Provieo builds the project as a live case-study page: slides, tables and findings you fill in. You bring the thinking.

Where did this project come from?

Provieo read the Merck posting and generated it on 2026-10-16. If you paste the same posting yourself you may get a different project, so two applicants don't show up with the same one.

Is Provieo connected to Merck?

No. This is an independent project idea based on a public posting. It does not guarantee an interview.