Okay, I've searched for full-time Data Scientist roles in Seattle, WA, requiring a Master's degree or higher in Math or related fields, with research experience and at least 3 years of experience. I have found 57 positions for you.

Sure. Here's the analysis:

Job Analysis:

This Data Scientist role at Outlier is fundamentally about bridging deep mathematical expertise with advancing generative AI quality. The core purpose is to use advanced math knowledge to evaluate and refine AI-generated content, ensuring factual accuracy and scientific rigor—tasks critical to the AI's ability to solve complex problems accurately. Beyond mere validation, the candidate will actively generate and rank math-related questions and responses, playing a key role in teaching AI models through expert human feedback. The responsibilities extend into nuanced evaluation, requiring discernment to assess subtle errors or gaps in AI reasoning. The requirement for a Master’s or PhD in Math with research credentials signals a need for candidates with strong analytical thinking, rigorous problem-solving, and ability to communicate complex ideas clearly in English. Candidates must balance technical depth with evaluative judgment, often operating with some autonomy amid ambiguous AI output quality. Success means consistently improving AI model reliability through expert feedback, advancing high-impact partnerships with leading AI companies, and potentially engaging in higher-level research initiatives. Given the remote, flexible nature and performance-linked pay, self-motivation and discipline are implied. The role demands a blend of scholarly rigor, practical evaluation acumen, and a growth mindset to thrive in shaping the forefront of generative AI intelligence.

Company Analysis:

Outlier is a cutting-edge platform within the AI data ecosystem, owned by Scale AI, that leverages a global network of experts to enhance the quality of AI models by infusing human expertise directly into the training data. Positioned as a crucial conduit between top AI labs (like OpenAI and Meta) and real-world expert insight, Outlier operates in a fast-evolving, innovation-driven industry sector. While it provides a remote-first, freelance-friendly work environment prioritizing reliability, transparency, and flexibility, it remains focused on delivering precision and scientific accuracy to its AI partners. The company culture likely prizes intellectual rigor, autonomy, and clear communication, appealing to experts who want impact without strict corporate constraints or traditional benefits structures. The role fits as an individual contributor with high visibility on quality outcomes, given the tight feedback loops with major AI clients. Strategically, hiring advanced math experts supports Outlier’s goal to maintain a competitive edge by continuously enhancing generative AI capabilities. Candidates should expect to integrate deeply into a specialized, mission-oriented community focused on shaping future AI through expert validation and correction, thriving best when comfortable with remote autonomy and driven by a passion for cutting-edge AI improvements.

Absolutely. Here are some mock interview questions that could come up:

• Behavioral: Tell me about a time when you identified a subtle mathematical error in a complex problem. How did you approach communicating that error and aiding in its correction?
• Behavioral: Describe a situation where you had to evaluate ambiguous or incomplete data. How did you navigate the uncertainty to reach a confident conclusion?
• Behavioral: How do you manage your workflow and motivation in a highly autonomous, remote work environment?
• Behavioral: Give an example of collaborating with a cross-disciplinary team or stakeholders unfamiliar with math. How did you ensure your evaluations were understood and actionable?
• Technical: How would you approach evaluating the factual accuracy and relevance of AI-generated mathematical content? What criteria or frameworks would you employ?
• Technical: Can you explain how you might design Math-related questions to test and improve an AI model’s understanding of advanced mathematical concepts?
• Technical: Describe your experience with research or industry tools that support mathematical problem solving and evaluation. How would you leverage these in this role?
• Technical: If given an AI-generated solution that is scientifically rigorous but unintuitive, how would you assess and rank its quality?
• Situational: Imagine you discover recurring inaccuracies in AI outputs regarding a specific math domain. What steps would you take to address this through your evaluations and feedback?
• Situational: If faced with conflicting assessments from multiple experts on the same AI-generated response, how would you handle this discrepancy?
• Situational: How would you prioritize your tasks if you had to support multiple projects with varying deadlines and complexity?
• Company Fit: Why are you interested in contributing your math expertise to improve generative AI models through Outlier?
• Company Fit: How do your professional values and approach to scientific rigor align with Outlier’s mission to enhance AI through expert feedback?
• Company Fit: What excites you most about working in a flexible, remote environment with a focus on cutting-edge AI technology?
• Questions for Interviewer: How does Outlier measure the impact of expert feedback on improving AI model performance?
• Questions for Interviewer: Can you describe the typical career progression or opportunities for advanced contributors within Outlier?
• Questions for Interviewer: What are the biggest challenges the math team currently faces in refining generative AI content, and how could this role help overcome them?