Senior Data Scientist – Genomics

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About the opportunity

Join Franklin by QIAGEN and help turn complex genomic and clinical data into insights that support clinical genetics and research. Franklin combines curated scientific knowledge, bioinformatics pipelines and AI-driven analysis to help users interpret and report genomic data at scale.

In this Senior Data Scientist role, you will take algorithms and machine-learning solutions from experimentation through production, working across genomic data, NLP and LLM-based applications. Partnering with software engineers, product managers and clinical genetics experts, you will tackle complex scientific problems where accuracy, reproducibility and reliability matter.

Your tasks include

• Designing, developing and productionizing machine-learning algorithms for genomic and clinical datasets, including variant classification and prioritization, phenotype-genotype matching and cohort-level analysis.
• Building and evaluating LLM-powered applications, RAG solutions and agentic workflows, from early prototypes through production deployment.
• Developing NLP and text-mining pipelines that transform scientific literature and other unstructured biomedical content into structured, actionable data.
• Translating biological, clinical and product challenges into robust and scalable technical solutions in partnership with software engineers, product managers and domain experts.
• Optimizing data-intensive algorithms using parallel and distributed computing approaches.
• Managing the end-to-end model lifecycle, including experimentation, validation, testing, deployment, monitoring and continuous improvement.
• Applying high standards of accuracy, reproducibility and production reliability while communicating technical findings and trade-offs clearly to different stakeholders.

Your profile

• 5+ years of professional experience in data science, machine learning or applied research, including hands-on experience deploying ML solutions into production.
• Strong Python expertise and practical experience with relevant data science and ML technologies such as NumPy, pandas, scikit-learn, PyTorch or TensorFlow.
• Strong foundations in statistics, machine learning and algorithm design, with the ability to translate mathematical and statistical approaches into maintainable production solutions.
• Hands-on experience with LLM applications, RAG or agentic workflows, combined with NLP or text-mining experience involving unstructured data.
• Experience with parallel or distributed computing and solid software-engineering practices, including Git, testing, code review and maintainable code.
• An MSc or PhD in Computer Science, Bioinformatics, Computational Biology, Data Science or another relevant quantitative discipline, or equivalent practical experience.
• Fluent English communication skills and the ability to collaborate effectively across technical and scientific disciplines. Experience in genomics, clinical genetics, bioinformatics or healthcare data, including genomic formats such as VCF or BAM/CRAM, would be an advantage.