Associate Data Scientist | Personalized Medicine | Deep Tech Startup

iLoF; Porto; Presencial; EUR 30 000 - 45 000; Tempo integral

Resumo da oferta

iLoF is seeking an Associate Data Scientist to develop methods that extract signals from biomedical data. This high-impact role involves collaborating with teams to improve signal quality and apply statistical methods in a fully onsite position in Porto.

Applicants should have a Master’s or PhD in a relevant field and 1-3 years of experience in data analysis. Strong foundations in applied statistics and communication skills are essential. Join a growing team at the forefront of biomedical innovation!

Qualificações

• Master’s degree or PhD in a relevant field.
• 1–3 years of experience on quantitative data problems.
• Strong foundation in applied statistics and predictive modelling.

Responsabilidades

• Support the data science workflow from problem framing to delivery.
• Analyse biomedical datasets to identify patterns.
• Help develop statistical and machine learning methods.
• Improve preprocessing pipelines and signal quality.

Conhecimentos

Applied statistics

Predictive modeling

Signal processing

Chemometric analysis

Data preprocessing

Communication skills

Formação académica

Master’s degree or PhD in Computer Science, Applied Math, Statistics, Physics, Engineering

Ferramentas

Raman spectroscopy

FTIR

Descrição da oferta de emprego

Associate Data Scientist | Personalized Medicine | Deep Tech Startup

• Full-time

At iLoF, you will help develop data science methods that extract reliable signals from biomedical and optical data. The role sits at the intersection of modelling, signal processing, and experimental science, with a focus on noisy, high‑dimensional, and biologically complex datasets.

You will work with raw data generated on in-house instruments, helping improve signal quality, acquisition protocols, and hardware performance. You will also apply chemometric and multivariate methods to spectroscopic data such as Raman and FTIR, turning complex measurement signals into more reliable and interpretable insights.

You will work closely with experimentalists, hardware specialists, and product teams, directly influencing experimental design, data interpretation, and technology development — creating a tight feedback loop between theory and practice.

This is a high‑impact role in a growing team. We are looking for someone who combines strong technical foundations, scientific curiosity, and the ability to learn and execute well in a fast‑moving environment.

*This is a fully onsite position, based at our Porto office

Key Responsibilities

• Support the data science workflow for specific projects, from problem framing to delivery.
• Analyse proprietary biomedical datasets to identify patterns, variability, and opportunities for improved performance.
• Help develop and apply statistical and machine learning methods for noisy, high‑dimensional, and often small‑sample data.
• Contribute to validation strategies, including uncertainty and robustness checks.
• Help improve preprocessing pipelines, signal quality, and acquisition protocols through work with raw data and instrumentation.
• Apply chemometric and multivariate analysis to spectroscopic data.
• Work cross‑functionally with experimental, hardware, and product teams.
• Communicate findings, trade‑offs, and recommendations clearly to technical and non‑technical stakeholders.
• Contribute to reusable analysis frameworks, documentation, and internal best practices.
• Stay up to date with developments in data science, computational modelling, and spectroscopy for clinical applications.
• Contribute to research publications, patents, and conference presentations where relevant.

Qualifications

• Master’s degree or PhD in Computer Science, Applied Math, Statistics, Physics, Engineering, or a related field. Exceptional candidates with relevant research or applied experience will also be considered.
• Around 1–3 years of experience in academia or industry working on quantitative data problems.
• Strong foundation in applied statistics, predictive modelling, and experimental design.
• Experience working with raw data alongside instrumentation, including optimisation of signal quality, acquisition protocols, or hardware performance.
• Experience in chemometric or multivariate analysis of spectroscopic data such as Raman, FTIR, or related high‑dimensional signal data.
• Strong data preprocessing, feature engineering, and exploratory analysis skills.
• Experience supporting validation strategies and interpreting model performance in ambiguous or noisy settings.
• Strong problem‑solving ability and comfort with incomplete, messy, or evolving data.
• Excellent written and verbal communication skills.
• Interest in mentoring and learning from senior colleagues is a plus.