Genomics Data Scientist: ML, NGS & Bioinformatics

Reed; Cambridge; Presencial; GBP 40.000 - 70.000; Jornada completa

Descripción de la vacante

Reed in Cambridge is seeking a Bioinformatician to join an innovative team focused on functional genomics, data science, and scalable analytics. You will tackle complex biological datasets, apply state‑of‑the‑art statistical methods and machine learning to research and product development, and collaborate with scientists to inform experimental design and interpretation.

The role emphasizes advancing genomics workflows, producing rigorous reports, and sharing knowledge across cross‑functional

Formación

• Proven track record in statistics, biostatistics, bioinformatics or genomics.
• Strong experience with ML, deep learning and statistical modelling.
• Advanced programming in Python and R (and optionally Julia).
• Experience with NGS/OMICS datasets and WGS workflows.
• Familiarity with bioinformatics databases and pipelines.

Responsabilidades

• Lead statistical and bioinformatics analyses on complex datasets.
• Develop statistical methods and computational algorithms.
• Apply ML/AI to research and product initiatives.
• Collaborate with lab scientists for experimental design and data interpretation.
• Analyze NGS and other high-throughput genomics data.
• Produce technical reports and present findings to stakeholders.
• Support knowledge sharing across research and technology teams.

Conocimientos

Statistics

Bioinformatics

Genomics

Machine Learning

Python

R

Herramientas

GitHub

TensorFlow

PyTorch

Scikit-Learn

Keras

Descripción del empleo

Reed in Cambridge is seeking a Bioinformatician to join an innovative team focused on functional genomics, data science, and scalable analytics. You will tackle complex biological datasets, apply state‑of‑the‑art statistical methods and machine learning to research and product development, and collaborate with scientists to inform experimental design and interpretation.

The role emphasizes advancing genomics workflows, producing rigorous reports, and sharing knowledge across cross‑functional