Women's Health Proteomics Data Science Intern

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

• Fertility benefits: Not verified
• Adoption assistance: Not verified
• Surrogacy assistance: Not verified

Mental health support

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Relocation assistance

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Childcare support

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Learning budget

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Verification

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Salary

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Market context

U.S. role benchmark (BLS OEWS)

$111,944 U.S. median for this role

Projected growth (BLS Employment Projections)

+13.7% - Much faster than average

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function

Data From the posting source

Seniority

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Work mode

Onsite From the posting source

In-office days

5 days From the posting source

Schedule

Shift type

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Weekend work

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Application

Cover letter

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Assessment

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Deadline

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Where they hire

State eligibility is not yet verified.

About this role

Women's Health Proteomics Data Science Intern Country Switzerland Location: Lausanne, Switzerland Work model: On site Contract type / duration: Internship - 6 months Start Date : September 2026 Deadline for applications: June 18 Be a force for good and transform your potential into something extraordinary. As a Proteomics Data Science Intern at Nestlé Research, Nestlé Institute of Health Sciences, your mission is to help uncover nutrition-related biomarkers by analysing large-scale proteomics and health data linked to women's health across key life stages. You will make a difference by collaborating closely with scientists, data specialists and cross-functional project teams to connect clinical, nutritional and phenotypic insights with molecular data. This is your opportunity to contribute to meaningful research while gaining hands on experience and mentorship to kick-start your career. How you'll make an impact as a Women's Health Proteomics Data Science Intern Applying and refining statistical methods to analyse proteomics and health data from large cohorts, including biobank-scale datasets. Identifying meaningful biomarkers that can deepen understanding of women's health, ageing and nutrition-related outcomes. Reviewing scientific literature and translating insights into clear recommendations for internal research discussions. Partnering with cross-functional research teams to combine proteomics, clinical, nutritional and phenotypic data and improve the relevance of your analyses. Delivering a final report or manuscript that shares your

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