Are you passionate about leveraging advanced data science techniques to uncover true causal relationships and drive impactful decision-making within a dynamic international organisation? We are searching for an experienced and driven Senior Data Scientist with deep knowledge of Causal Inference to join our high-performing team in Lisbon. If you thrive in analytical environments, enjoy tackling complex data challenges, and are ready to influence business direction on a global scale, we invite you to apply!
Key Responsibilities – Senior Data Scientist Causal Inference Roles in Lisbon- Design, develop, and implement rigorous causal inference models to measure the true effects of interventions and business strategies across diverse domains.
- Utilise advanced statistical techniques such as propensity score matching, difference-in-differences, instrumental variables, regression discontinuity, and other modern causal methodologies.
- Collaborate closely with cross-functional teams—including Product, Engineering, and Business Analytics—to translate business problems into causal questions and robust analytical solutions.
- Work with large, complex data sets to extract actionable causal insights and inform strategic company initiatives.
- Effectively communicate methodologies, results, and business implications to both technical and non-technical audiences, including senior stakeholders.
- Contribute to the deployment and scalability of causal models in production environments, ensuring reliability and robustness.
- Mentor junior team members, fostering knowledge sharing and best practices in causal inference and advanced analytics.
- Master’s or PhD in Statistics, Economics, Data Science, Computer Science, Mathematics, or a related quantitative discipline.
- Extensive professional experience (4+ years) as a Data Scientist, Statistician, or Economist with a focus on causal inference methodologies.
- In-depth expertise in the application of causal inference methods, including but not limited to propensity score techniques, natural experiments, and experimental design.
- Advanced proficiency in Python or R and relevant data science and statistical libraries (e.g., causalinference, DoWhy, EconML, scikit-learn, pandas, NumPy).
- Practical experience with experimental design, A/B testing, and the analysis of observational data.
- Ability to structure ambiguous business problems and develop innovative, data-driven solutions.
- Excellent communication, problem-solving, and interpersonal skills.
- Fluency in English is required; knowledge of Portuguese is advantageous but not essential.
- Secure a rewarding position with a globally recognised, technology-driven organisation based in the vibrant city of Lisbon.
- Competitive salary and attractive performance-based bonuses and benefits.
- Comprehensive private healthcare coverage to support your well-being.
- Inclusive relocation package for those moving to Portugal, with assistance for settling in.
- Access to state-of-the-art analytics tools, continuous professional development, and training opportunities in advanced causal inference and data science.
- Work within a diverse, innovative, and supportive team environment that values different perspectives and approaches.
- Clear pathways for career progression and personal growth within the data science community.
- Drive high-impact business decisions as part of an ambitious analytics team shaping the future of a global organisation.
- Engage in ongoing learning and diverse analytical challenges that will stretch your expertise and skill set.
- Benefit from an inclusive company culture that encourages open collaboration, experimentation, and knowledge-sharing.
- Enjoy the exceptional lifestyle, culture, and international atmosphere that Lisbon offers while advancing your impact as a Senior Data Scientist.
If you are ready to advance your career in causal inference, contribute to world-class analytics, and make a difference in a thriving international business, apply today to become a Senior Data Scientist – Causal Inference with us in Lisbon!