Senior Data Scientist: Physical Signals & Biomed Data (Remote)

iLoF; Porto; Hybrid; EUR 65,000 - 100,000; Full time

Benefits offered by this job

Weekly happy hours

Free coffee and snacks

Flexible working hours

Job summary

iLOF is seeking a Senior/Lead Data Scientist in Porto to bridge physical sciences, data science, and experimental technologies. You will own complex analytical projects and guide methodological decisions across initiatives.

You will work with scientists and engineers to co-design experiments, turn raw measurements into biological insights, and develop robust analytical workflows with reproducible results.

Qualifications

• PhD in Physics, Applied Physics, Engineering or related quantitative discipline (Master's considered with substantial experience).
• Strong experience analysing physical signals or complex measurement-based datasets.
• Sound understanding of signal processing, statistical analysis and machine learning.
• Demonstrated ability to design investigations, work with raw experimental data and assess explanations.

Responsibilities

• Own analytical work from problem framing to interpretation of results.
• Prototype and evaluate signal-processing, preprocessing and modelling approaches for complex datasets.
• Identify sources of technical variation and artefacts and develop methods to correct them.
• Collaborate with experimental scientists to design targeted experiments and improve data quality.
• Mentor colleagues and contribute to technical reviews and publications.

Skills

Signal processing

Statistical analysis

Machine learning

Scientific programming

Education

PhD in Physics/Engineering/Applied Physics

MSc with substantial relevant experience

Job description

iLOF is seeking a Senior/Lead Data Scientist in Porto to bridge physical sciences, data science, and experimental technologies. You will own complex analytical projects and guide methodological decisions across initiatives.

You will work with scientists and engineers to co-design experiments, turn raw measurements into biological insights, and develop robust analytical workflows with reproducible results.