Anthropic Data Analyst Interview Guide

Demonstrate understanding of both approaches. Discuss how constitutional AI uses principles to guide model behavior, the advantages over pure human feedback, and the remaining challenges.

Think about behavioral testing, probing internal representations, adversarial evaluation, and the fundamental difficulty of detecting deception. Show original thinking about an open research problem.

Show that safety thinking is natural for you. Describe how you identified the risk, communicated it to stakeholders, and drove a resolution. Anthropic wants people who proactively think about failure modes.

Show nuanced thinking. Discuss Anthropic's view that building frontier models is necessary for safety research, while safety must advance alongside capabilities. Avoid simplistic positions.

Consider automated evaluation, human review pipelines, anomaly detection, and incident response. Show understanding of the unique challenges of monitoring AI systems compared to traditional software.

Anthropic's empirical approach to safety means updating beliefs based on evidence. Show intellectual humility and willingness to let data change your mind, even when it's uncomfortable.

Discuss a specific alignment problem with depth — scalable oversight, interpretability, reward hacking, or deceptive alignment. Show you've thought carefully about the problem space.

This is core to Anthropic's mission. Discuss training approaches, evaluation methods, and the fundamental challenges of ensuring AI honesty. Show understanding of current research and open questions.

Anthropic values interdisciplinary collaboration. Show how working with people from different backgrounds — safety researchers, ML engineers, policy experts — led to insights neither group would have reached alone.