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Statistical Geneticist

Job in Milan, Lombardy, Italy
Listing for: Altro
Full Time position
Listed on 2026-09-01
Job specializations:
  • Research/Development
    Data Scientist, Research Scientist, Genetics / Genomics, Biomedical Science
Salary/Wage Range or Industry Benchmark: 45000 - 55000 EUR Yearly EUR 45000.00 55000.00 YEAR
Job Description & How to Apply Below
Build the science that shapes the future of human health.

Final date to receive applications: 01.09.2026

Join a place where ambitious science thrives
Human Technopole (HT) is an international research institute dedicated to advancing human health through interdisciplinary life science research. Located in Milan's MIND innovation district, HT brings together scientists from around the world to tackle fundamental questions in biology using state-of-the-art technologies and large-scale datasets.

Within this mission, the  Biostatistics Unit  of the  Centre for Genomics - Population and Medical Genomics Programme  provides statistical expertise across research projects, supporting the analysis of complex human genetic and multi-omics data to uncover the biological mechanisms underlying health and disease.

We are seeking a  Statistical Geneticist  to contribute to innovative population and medical genomics research by developing robust analytical strategies and collaborating with multidisciplinary teams with a particular focus on defining and analysing complex multivariate phenotypes, such as multicellular programmes and multimorbidity measures.

Your mission
As a Statistical Geneticist, you will provide statistical and computational expertise to support cutting-edge genomic research across the Centre.

You Will

Lead and conduct advanced statistical genetics analyses, including GWAS, polygenic risk scores, fine-mapping, heritability estimation, genetic correlation, Mendelian randomisation, multi-omics integration, and related computational analyses using large-scale genetic, genomic, multi-omic, phenotypic, clinical, imaging, and electronic health record datasets.

Advise research group leaders, collaborators, PhD students, postdoctoral researchers, and junior analysts on appropriate statistical, genetic, epidemiological, computational, machine-learning, and AI-based approaches to address biological and clinical research questions.

Translate complex research questions into robust and reproducible analytical strategies, including study design, data requirements, model selection, pipeline development, sensitivity analyses, documentation, interpretation of results, and transparent reporting.

Develop, apply, evaluate, and validate methods for deriving embeddings, latent variables, scores, and other quantitative representations of multivariate traits, such as multicellular programmes, disease trajectories, multimorbidity patterns, and complex phenotypic profiles, ensuring biological interpretability and relevance to downstream genetic analyses.

Review analytical plans, code, results, and interpretations to ensure methodological rigour, reproducibility, data governance, appropriate reporting and best practice in version control, workflow management, documentation, while keeping abreast of emerging methods in statistical genetics, computational biology, machine-learning, and AI.

Grow Your Skills
You will work alongside internationally recognised researchers in statistical genetics, genomics and computational biology, contributing to high-impact research while developing your expertise through collaboration, mentoring and continuous learning.

Human Technopole supports career development through dedicated training opportunities, scientific seminars and an international research environment.

Essential
What you'll bring

PhD in Statistical Genetics, Biostatistics, Statistics, Epidemiology, Bioinformatics, Computational Biology or another relevant quantitative discipline.

Hands-on experience in statistical genetics and the analysis of large-scale human genetic and genomic datasets.

Strong programming skills in R and/or Python and experience working in Linux/HPC environments.

Experience with statistical genetics software (e.g. PLINK, REGENIE, GCTA or equivalent).

Fluency in spoken and written English.

Preferred

Experience with multi-omics and/or single-cell datasets.

Experience applying machine-learning or AI approaches to biological data.

Experience with reproducible research practices and version control (e.g. Git/Git Hub).

Previous experience mentoring or supervising junior researchers.

Organizational and social skills

Ability to…
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