Genomics Data Analyst 

Computational and Statistical Genomics Group | FIMM | University of Helsinki

About the group

The successful candidate will collaborate closely with the FinnGen consortium, one of the world’s largest genomic research initiatives, and contribute to a vibrant, international team with access to unique datasets and world-class computational resources.

The Computational and Statistical Genomics group at FIMM, led by Simone Rubinacci, develops methods for large-scale human genomics and statistical genetics. Our work spans algorithm development, cloud computing, and population-scale sequencing to understand human disease and genetic variation better.

What you will work on

This role sits at the core of our data infrastructure. Your primary focus will be on ensuring that our WGS and WES data are clean, harmonized, and analysis-ready. Over time, you will also have the opportunity to contribute to downstream genomic analyses and methods work.

•    Processing, quality control, and harmonization of large-scale Whole Genome and Whole Exome Sequencing datasets.

•    Building and maintaining scalable, reproducible workflows on HPC and cloud platforms, including Google Cloud.

•    Variant calling and QC for SNVs, indels, and structural variants.

•    Collaborating with FinnGen network researchers and international partners on joint analyses.

•    Over time: contributing to haplotype phasing, imputation, population genetics analyses, and integration of long-read sequencing and pangenome approaches.

What we are looking for

We welcome candidates from bioinformatics, computational biology, statistical genetics, computer science, or any related quantitative field. A PhD is preferred for the postdoctoral level, but strong candidates with equivalent research or industry experience are very welcome to apply. What matters most is your ability to handle and analyze large-scale genomic data and your enthusiasm for the work. You do not need to tick every box. If you have strong experience in WGS/WES data handling and are eager to learn, we want to hear from you.

Core skills (the focus of this role)

These are the skills most central to the day-to-day work. We expect candidates to have solid hands-on experience in most of these:

•    Processing and analyzing WGS and/or WES data, including alignment, duplicate marking, and base quality recalibration (e.g. using BWA, GATK, Samtools).

•    Variant calling and quality control for SNVs and indels; familiarity with common QC metrics such as coverage, Ti/Tv ratios, and contamination checks.

•    Writing and maintaining reproducible analysis pipelines using workflow managers such as Nextflow, Snakemake, or WDL/Cromwell.

•    Comfortable working in Linux/HPC environments with job schedulers (SLURM or similar) and shell scripting.

•    Scripting in Python and/or R for data processing, QC visualization, and analysis.

•    Experience with version control (Git) and containerization tools (Docker or Singularity) for reproducible environments.

Useful experience (good to have, not required)

Any of the following would be a bonus, but please do not be put off if you have only some of these or none at all:

•    Cloud computing, particularly Google Cloud (GCP); experience with cloud storage and scalable compute is a plus.

•    Structural variant calling and quality control (e.g. using Manta, PBSV, or similar tools).

•    Haplotype phasing or genotype imputation (e.g. SHAPEIT, Beagle, or Michigan Imputation Server).

•    Population genetics concepts such as population stratification, relatedness inference, or GWAS.

•    Exposure to long-read sequencing data (ONT or PacBio) or emerging pangenome reference approaches.

•    Familiarity with large-scale genomic databases or biobank data (e.g. FinnGen, UK Biobank, gnomAD).

•    Knowledge of data governance and security practices for controlled-access human genomic data.

What We Offer

This is a unique opportunity to work on some of the most exciting genomic datasets in the world, in a group that values both rigorous science and a supportive team culture. You will be part of an international, collaborative environment and have room to develop your skills and research profile.

•       Salary: €4,500–5,500/month gross, in accordance with the University of Helsinki salary system (YPJ), based on qualifications, experience, and performance.

•       Contract: Fixed-term for two years, with the possibility of extension depending on funding and performance.

•       Research environment: Access to FinnGen’s and other large omics data sources, the Finnish IT Center for Science (CSC), and extensive Google Cloud infrastructure.

•       Career development: You will have the opportunity to contribute to high-impact publications, present at international conferences, and develop expertise at the intersection of genomics infrastructure and large-scale data analysis.

•       Mentorship: Close day-to-day collaboration with Simone Rubinacci and the broader group, with regular feedback and support for your scientific and professional growth.

•       Location: FIMM is based at the Meilahti medical campus in Helsinki, one of Europe’s most livable cities, within a dynamic and international research community.

How to Apply

Please submit your application through the University of Helsinki recruitment system by July 17th, 2026. Your application should include:

●    A cover letter describing your experience with large-scale sequencing data and what draws you to this role. We are most interested in hearing about your own work and ideas.

●    A CV and list of publications, preprints, or a link to your GitHub profile.

●    Contact information for two references.

For more information regarding the position or the research goals of the group, please contact Dr. Simone Rubinacci at simone.rubinacci(at)helsinki.fi.

A diverse and equitable study and work culture is important to us. That is why we do our best to promote an inclusive university community. We encourage all qualified applicants from diverse backgrounds to apply for our positions.

Click this link to read about accessibility and inclusivity at our University.

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