Data Scientist PhD

Causal Foundry; Barcelona; On-site; EUR 55,000 - 80,000; Full time

Job summary

Causal Foundry in Barcelona seeks a PhD graduate to join our data science team. You will analyse real-world data, develop and evaluate models, and design analyses that help partners make better decisions.

This hands-on role focuses on translating findings into tangible improvements. No professional experience beyond your PhD is required; we welcome candidates moving directly from doctoral research into industry.

Qualifications

• PhD in a quantitative field (stats, CS, maths, physics)
• Strong foundation in statistical modelling and ML
• Experience applying quantitative methods to data during PhD or projects
• Strong Python and SQL skills
• Experience using AI agents and coding tools for data workflows with validation
• Understanding of model evaluation and causal inference
• Reproducibility, documentation and version control

Responsibilities

• Explore, clean and analyse complex longitudinal datasets to understand patterns and insights.
• Translate questions into well-defined analytical or causal problems.
• Build and validate models for forecasting, risk stratification or recommender systems.
• Design and analyse experiments and apply causal inference methods.
• Account for uncertainty, bias and missing data in results.
• Develop reproducible analyses and clearly documented code in Python and SQL.
• Collaborate with ML engineers and domain experts to turn findings into solutions.
• Communicate methods and findings clearly to colleagues and partners.
• Contribute analyses and technical writing to scientific outputs.

Skills

Python

SQL

Statistical modelling

Education

PhD in statistics / computer science / mathematics / physics

Tools

Claude Code

Codex

Cursor

Job description

Causal Foundry is looking for a PhD graduate to join our data science team in Barcelona. You will analyse real-world data, develop and evaluate models, and design analyses that help our partners make better decisions. The role centres on hands-on data science: turning complex questions into rigorous analyses and translating findings into practical improvements.

No professional experience beyond your PhD is required. We welcome candidates moving directly from doctoral research into industry.

The company

Causal Foundry (CF) builds AI products to solve operational challenges across global health, supply chains, industry and education. Our AI platform, Kenkai, helps digital applications use advanced prediction and personalisation methods to improve decisions and outcomes.

Our work combines statistical modelling, machine learning, causal inference and adaptive interventions, including contextual and restless bandits. We collaborate with partners to understand their challenges, develop appropriate solutions and measure their impact.

What you will do

• Explore, clean and analyse complex, longitudinal datasets to understand patterns, assess data quality and identify useful insights.
• Translate product, health and operational questions into well-defined analytical, predictive or causal problems.
• Build and validate models for forecasting, risk stratification, personalisation, resource allocation and decision support.
• Design and analyse experiments, and apply causal inference methods to understand what works, for whom and under which conditions.
• Evaluate results carefully, accounting for uncertainty, bias, missing data and the limitations of available evidence.
• Develop reproducible analyses and clearly documented code in Python and SQL.
• Collaborate with ML engineers, data engineers and domain experts to turn analytical findings and models into practical solutions.
• Communicate methods, findings and limitations clearly to technical colleagues and partner teams.
• Use Causal Foundry products in your work and help improve its analytical capabilities, data quality and usability through feedback to Engineering.
• Contribute analyses and technical writing to scientific papers and other research outputs.
• Your projects may include work with public-sector and health partners in Rwanda. Some partner collaboration may involve travel.

What we are looking for

• A PhD in statistics, computer science, mathematics, physics or a closely related quantitative field.
• A strong foundation in statistical modelling and machine learning, with depth in one or more relevant methods.
• Experience applying quantitative methods to data through your PhD research or other academic projects.
• Strong Python skills and the ability to work with data using SQL.
• Experience using AI agents and coding tools such as Claude Code, Codex or Cursor for data exploration, analysis, modelling and research workflows, with careful validation of generated code and results. Experience gained during your PhD or personal projects is sufficient
• An understanding of sound model evaluation, experimental design and the principles of causal inference.
• Care for reproducibility, clear documentation and version control.
• The ability to work through ambiguous questions, explain methodological choices and recognise the limitations of an analysis.
• Curiosity, initiative and a collaborative approach to solving problems.
• Experience with contextual bandits or reinforcement learning, time-series forecasting, survival analysis, Bayesian methods, optimisation or recommender systems.
• Publications, open-source contributions or other examples of rigorous, reproducible research.

Why Causal Foundry

• Apply your research skills to real operational decisions and problems with social impact.
• Explore advanced methods and see how they perform when applied, deployed and measured in practice.
• Work closely with colleagues across data science, engineering and domain disciplines.
• Build experience across the full data science process, from defining a question to evaluating the impact of a solution.
• Collaborate with people and partners across cultures and geographies.

This is an on-site role in our Barcelona office. Working hours are 10 am to 7 pm.