What is a Data Scientist?

At Anthropic, the role of a Data Scientist is pivotal to our mission of building reliable, interpretable, and steerable AI systems. Unlike traditional data science roles that may focus primarily on business analytics or simple forecasting, our Data Scientists work at the frontier of large language model (LLM) development and safety. You are not just analyzing data; you are creating the feedback loops and measurement systems that define how Claude interacts with the world.

This position sits at the intersection of research and engineering. You will be responsible for rigorously evaluating model performance, designing metrics to quantify "helpfulness" and "harmlessness," and building the data infrastructure that powers our research velocity. Whether you are working on Constitutional AI, interpreting model behaviors, or scaling our training data pipelines, your work directly influences the safety and capability of systems deployed to millions of users.

We look for individuals who can blend deep statistical rigor with strong software engineering capabilities. You will tackle complex, ambiguous problems—such as how to measure hallucination rates or how to detect subtle biases in training data—and turn them into actionable code and insights. This is a role for those who care deeply about the long-term societal impact of AI and are ready to apply their technical skills to ensure that impact is positive.

Getting Ready for Your Interviews

Preparing for an interview at Anthropic requires a shift in mindset. We value raw technical ability and the capacity to move fast. You should approach your preparation by focusing on speed, accuracy, and engineering fundamentals, rather than just memorizing machine learning theory.

You will be evaluated on the following key criteria:

Software Engineering Proficiency – We expect our Data Scientists to write production-quality code. Interviewers will assess your fluency in Python, your ability to use standard libraries without constantly referencing documentation, and your grasp of algorithmic complexity. You must demonstrate that you can build tools, not just run scripts.

Practical Problem Solving – We look for candidates who can take a vague requirement and translate it into a working solution under time pressure. This involves understanding the constraints of a dataset, choosing the right data structures, and implementing a solution that scales.

Statistical Intuition – Beyond coding, you must demonstrate a strong grasp of probability and statistics. You should be able to explain why a metric is appropriate for a specific problem, how to handle outliers in high-stakes data, and how to design experiments that yield statistically significant results.

Mission Alignment – Anthropic is an AI safety company first. We evaluate how you think about the risks of deployed AI models. You should be prepared to discuss how data science can be used to mitigate these risks and align models with human values.

Interview Process Overview

The interview process for the Data Scientist role is rigorous and structured to test your practical skills in a realistic environment. Generally, the process begins with an initial screen, followed by a substantial technical assessment, and culminates in a virtual onsite loop. Unlike some research roles that prioritize publication history, our Data Scientist interviews heavily weigh hands-on coding ability and engineering velocity.

Candidates often report that the technical screens are distinctively fast-paced. You should expect assessments that test your ability to write code quickly and accurately. The philosophy here is to simulate the actual work environment: we often need to spin up analyses or pipelines rapidly to answer critical research questions. Consequently, the process can feel more like a Software Engineering interview than a traditional academic data science discussion.

The timeline above illustrates the typical flow from application to final decision. Use this visual to plan your study schedule; specifically, ensure your coding skills are sharp before the Technical Screen and CodeSignal stages, as these are often the primary filters. Note that the "Onsite" stage is comprehensive, covering coding, system design, and behavioral alignment in back-to-back sessions.

Deep Dive into Evaluation Areas

To succeed, you must be prepared for a technical bar that is higher than the industry average for Data Science roles. Based on candidate feedback, there is a significant emphasis on general coding and algorithms, sometimes even more so than specific LLM architectures.

Coding & Algorithms (The Core Filter)

This is the most critical area of your preparation. Candidates frequently report facing CodeSignal assessments consisting of multiple questions that increase in difficulty. A unique feature of these assessments is that questions often build upon one another—you must solve part A to solve part B.

Be ready to go over:

• Data Structures – Proficiency with arrays, hash maps, heaps, and graphs is essential.
• Algorithmic Efficiency – You must understand Big O notation and optimize your code for runtime and memory.
• Python Standard Library – Mastery of built-in libraries (e.g., collections, itertools, re) is crucial for speed.
• Advanced concepts – Dynamic programming or complex graph traversals may appear in the final questions of the assessment.

Example questions or scenarios:

• "Given a stream of data logs, parse and aggregate the data to find anomalies, then optimize the aggregation for a larger dataset."
• "Implement a simulation where the rules of the environment change in the second and third parts of the question."
• "Solve a matrix manipulation problem where the constraints tighten with each follow-up question."

Data Manipulation & Analysis

While strong engineering is required, you are still a Data Scientist. You will be tested on your ability to work with data programmatically. This is less about SQL queries (though you should know SQL) and more about using Pandas or NumPy to manipulate dataframes and perform vectorised operations.

Be ready to go over:

• Data Cleaning – Handling missing values, outliers, and malformed text data.
• Vectorization – Writing code that avoids slow loops by using library-native vector operations.
• Metric Design – creating custom metrics to evaluate model performance based on raw output logs.

Example questions or scenarios:

• "Here is a raw dataset of model outputs. Write a script to calculate the 'helpfulness' score based on these specific heuristic rules."
• "Transform this nested JSON dataset into a flat table suitable for analysis, handling all edge cases."

Machine Learning Fundamentals

Expect questions that probe your understanding of how models learn and how to evaluate them. While you might not be asked to code a Transformer from scratch, you need to understand the lifecycle of an ML project.

Be ready to go over:

• Evaluation Metrics – Precision, Recall, F1, ROC-AUC, and when to use which.
• Experimentation – A/B testing, hypothesis testing, and statistical significance.
• Model Bias – Detecting and mitigating bias in training datasets.

The word cloud above highlights the most frequently discussed topics in our interview feedback. Notice the prominence of Python, Coding, and Algorithms alongside data-specific terms. This reinforces the need to prioritize software engineering skills in your preparation strategy.

Key Responsibilities

As a Data Scientist at Anthropic, your day-to-day work will be deeply integrated with the engineering and research teams. You will not be working in a silo; you will be building the tools that allow us to understand our models.

Your primary responsibility will be measuring and improving model performance. This involves writing code to process massive datasets of model interactions, defining the ground truth for safety benchmarks, and creating automated pipelines that flag regression in model capabilities. You will often work with "messy" data generated by LLMs, requiring you to write robust parsers and cleaning scripts.

Collaboration is key. You will work closely with Research Scientists to operationalize their theories. For example, if a researcher proposes a new method for "Constitutional AI," you might be the one to build the data pipeline that selects the training examples to enforce that constitution. You will also interface with Product teams to translate user feedback into quantitative metrics that guide the next training run.

Role Requirements & Qualifications

We are looking for candidates who bridge the gap between a Researcher and a Software Engineer.

• Technical Skills (Must-Have):

  • Expert-level Python: You must be able to write clean, modular, and efficient code. This is non-negotiable.
  • Algorithmic Competency: Familiarity with LeetCode-style problems (Medium to Hard difficulty) and standard data structures.
  • Data Libraries: Deep familiarity with Pandas, NumPy, Scikit-learn, and SQL.

• Experience Level:

  • Typically, we look for candidates with experience in high-growth tech environments or research labs where rigor and speed are balanced.
  • A background in Physics, Math, Computer Science, or a related quantitative field is common.

• Soft Skills:

  • Communication: The ability to explain complex statistical concepts to non-experts.
  • Autonomy: We operate with a high degree of trust; you must be able to unblock yourself and drive projects forward.

• Nice-to-Have Skills:

  • Experience with Large Language Models (LLMs), NLP, or Reinforcement Learning (RLHF).
  • Experience with distributed computing frameworks (e.g., Ray, Spark) for large-scale data processing.

Common Interview Questions

The following questions are representative of what you might face. They are drawn from candidate feedback and are designed to test your ability to think on your feet. Do not memorize answers; instead, practice the process of deriving the solution.

Coding & Implementation

These questions test your raw coding speed and ability to follow complex instructions.

• "Write a function to parse a log file containing model conversation history and extract specific metadata fields."
• "Given a grid of values representing a dataset, implement an algorithm to find the longest path of increasing values."
• "Implement a simplified version of a tokenizer that handles specific edge cases in text strings."
• "Create a class that manages a data stream and calculates the moving average and median in constant time."

Analytical & Case Studies

These questions assess how you apply data science to real-world Anthropic problems.

• "How would you measure the 'creativity' of a language model? Define the metric and explain how you would collect the data."
• "We noticed a spike in user reports regarding 'lazy' model responses. How would you investigate this using data?"
• "Design an experiment to test if a new safety intervention has reduced harmful outputs without hurting helpfulness."

Behavioral & Values

• "Why do you want to work on AI safety specifically?"
• "Describe a time you had to compromise on code quality to meet a deadline. How did you handle the technical debt?"
• "Tell me about a complex technical idea you had to explain to a non-technical stakeholder."

These questions are based on real interview experiences from candidates who interviewed at this company. You can practice answering them interactively on Dataford to better prepare for your interview.

Frequently Asked Questions

Q: How difficult is the coding assessment compared to other data science roles? The coding bar at Anthropic is significantly higher than average. Many candidates are surprised by the focus on LeetCode-style algorithms and the requirement for speed. You should prepare as if you are interviewing for a backend software engineering role.

Q: Will I be asked about the architecture of Transformers or LLMs? While you should understand the basics of how LLMs work, recent interview feedback suggests the process is heavily weighted toward general software engineering and data manipulation skills. Do not neglect core coding practice in favor of reading research papers.

Q: What is the work culture like for this role? The culture is intense, collaborative, and mission-driven. Ratings indicate excellent career growth and compensation, but the work-life balance can be demanding due to the pace of the field. You will be working alongside some of the smartest people in the industry on problems that have never been solved before.

Q: Is this a remote role? Anthropic generally operates with a hybrid model, often centered around our hubs. Specific expectations will be discussed during the recruiter screen, but being present to collaborate with the research team is highly valued.

Other General Tips

• Master the "Build-Upon" Format: In the technical assessment (often CodeSignal), you will likely face a multi-part question. Read the prompt carefully. Good code structure in Part 1 will save you massive amounts of time in Part 4. If you write "spaghetti code" early on, you will struggle to extend it.
• Speed is a Skill: Practice solving medium-difficulty coding problems against a timer. Being correct is not enough; you must be correct quickly to finish all questions in the allotted time.
• Know the Mission: Read the "Constitutional AI" paper or Anthropic’s core views on AI safety. Being able to reference these concepts during behavioral rounds demonstrates deep interest and alignment.
• Python Libraries: Be comfortable with the entire Python standard library. Knowing how to use Counter, defaultdict, or heapq without looking them up can give you the speed edge you need.

Summary & Next Steps

Becoming a Data Scientist at Anthropic is an opportunity to shape the trajectory of artificial intelligence. You will be challenged to code better, think deeper, and work faster than you likely have before. The role demands a unique combination of software engineering excellence and statistical insight, all grounded in a commitment to safety.

To prepare, shift your focus toward intensive coding practice. Ensure your Python skills are production-ready and that you can manipulate data structures with ease. Review your statistics fundamentals, but prioritize your ability to implement solutions in code. This preparation will not only help you pass the interview but will also set you up for success in a role that is as technical as it is strategic.

The compensation data above reflects the high value Anthropic places on this role. Packages typically include a competitive base salary and significant equity, rewarding the high level of technical rigor required. We encourage you to approach this process with confidence—your skills in data and engineering are the keys to building safer AI systems. Good luck!