Causality Researcher

We are partnered with a well-funded Causal-AI Pharmaceutical company at the forefront of the next generation of artificial intelligence. They are expanding their research team in Barcelona and are looking for an enthusiastic, curious, and adaptable individual to join them in this highly innovative & dynamic environment.

The Role

As a Causality Research Scientist, your primary focus will be on fundamental research and its application to complex real-world problems. You will be responsible for developing new ideas, improving foundational methods, and conducting comprehensive data analysis using causal methods. Your work will directly impact observational-longitudinal studies in medicine and chemistry, and you will collaborate closely with field experts.

We are looking for a specific kind of researcher. This role is for someone who loves to think, reflect, and then produce. We value deep, critical thinking and the ability to build new ideas independently, not just the ability to execute memorized code.

Key Responsibilities

• Develop and improve new foundational methods in causal inference.

• Conduct comprehensive data analysis on observational-longitudinal studies.

• Collaborate with domain experts to translate abstract scientific ideas into concrete results.

• Organize and write research for publication in international, peer-reviewed journals.

• Engage in the academic community by refereeing, presenting, and discussing new research.

What we are looking for:

• Are a true problem-solver with a high degree of curiosity and adaptability.

• Are passionate about creating the future of AI.

• Have a proven ability to transform abstract concepts into concrete, published research.

• Are honest, self-aware of your limitations, and capable of managing a full research workload.

Required Qualifications

• A strong track record in research, evidenced by peer-reviewed publications.

• Proficiency in Python.

• Knowledge of basic Machine Learning (ML) and Deep Learning (DL) algorithms.

Desirable

• A research background in Mathematics, Physics, Chemistry, or Biology is highly preferred over

a background in Computer Science or AI research.

• Strong knowledge of causal reasoning, do-calculus, and related simplification algorithms.

• Experience with backend coding.