About Allos

Allos is a Causal-AI Pharmaceutical company at the forefront of the next generation of AI with embedded causality. We are expanding our research team and are looking for an enthusiastic, curious, and adaptable individual to join us in this highly 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.

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.

Who we are looking for

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.

You are the right fit if you:

• 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.

Preferred Qualifications

• 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.