---
esco_uri: http://data.europa.eu/esco/skill/e464363b-8346-421d-9035-52118b76d8c8
esco_label: "computational biology"
relation: optional
onet_soc: 15-2051.00
source: model-knowledge
confidence: high
qa_count: 0
generator: gemma3:27b (prompt-designed and spot-checked by Claude)
generated: 2026-07-10
---

# computational biology — Data Scientist

As a Data Scientist, 'Computational Biology' isn’t about *becoming* a biologist; it's about applying your data science toolkit to biological datasets. Think beyond standard tabular data – you'll frequently encounter sequence data (DNA, RNA, proteins), genomic variation calls (SNPs, indels), gene expression matrices, and imaging data. Daily tasks involve cleaning, transforming, and analyzing these complex formats, often requiring custom feature engineering tailored to biological meaning (e.g., calculating GC content, identifying protein domains). You'll build models for things like predicting protein structure, identifying disease biomarkers from transcriptomics, or understanding drug response based on genomic profiles.

Typical tools extend beyond the usual Python/R stack. Expect to use Bioconductor (R), specialized Python libraries like NumPy, SciPy, Pandas *plus* BioPython, scikit-learn for machine learning applied to biological features, and potentially workflow management systems like Nextflow or Snakemake to handle large-scale analyses common in genomics.  Cloud computing (AWS, GCP, Azure) is almost essential due to dataset sizes. Good computational biology work for a Data Scientist isn't just statistically sound; it’s *biologically plausible*. You need enough domain knowledge to validate results – a model predicting nonsense mutations as beneficial needs immediate scrutiny.

A major pitfall is treating biological data like any other dataset.  Assumptions about independence, stationarity, and even the meaning of 'noise' often don't hold. Batch effects in high-throughput experiments are notorious. Another challenge is interpretability – complex models can be accurate but offer little insight into underlying biological mechanisms. Prioritizing explainable AI (XAI) techniques becomes crucial.  Furthermore, reproducibility is paramount; detailed documentation of data provenance and analysis pipelines is non-negotiable.

What 'good' looks like: a Data Scientist with strong computational biology skills can translate biological questions into analytical problems, select appropriate methods, critically evaluate results in the context of existing biological knowledge, *and* communicate findings clearly to both technical and non-technical audiences. They aren’t just running algorithms; they are contributing to scientific discovery by leveraging data science principles.

## Weitere Anreicherung

Stage-2 source for future practitioner grounding: arXiv cs.LG/stat.ML (ML preprints).
