Data Science Tech Lead Manager, Model Experience

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role As part of our growing Data Science and Analytics team, you will play an instrumental role in our company's mission of building safe and beneficial artificial intelligence by driving data-informed decision making across our organization. You bring experience from high-bar teams and are eager to help shape the norms and best practices of a growing data science team as Anthropic continues to scale. In this unique company, technology, and moment in history, your work will be critical to informing our strategy as we deploy safe, frontier AI at scale to the world. You'll be the senior data science leader for Model Experience and a strategic thought partner to product, engineering, and research leaders. Working where frontier AI research reaches the public, you'll help shape model roadmap and launch decisions, bringing a point of view on how users experience our models and what good looks like beyond benchmark scores. As a tech lead manager, you'll also hire and manage a team of data scientists, setting their agenda, personally leading the highest-stakes analyses, and raising the bar for how data shapes model and product decisions. Responsibilities: Act as a strategic thought partner to product, engineering, and research leaders, helping shape model roadmap priorities, launch decisions, and the major bets the team makes Set the data science agenda for Model Experience, deciding where the team can have the most impact on model development and launch decisions and what it should deprioritize Hire, manage, and develop a team of data scientists, coaching them to move from answering questions to shaping decisions Frame the most important and ambiguous questions about model quality and user experience, size the opportunities, and make clear recommendations, including when they challenge the current direction Define how Model Experience measures success, establishing the core metrics and measurement frameworks across external products and internal testing that align teams and leadership on what matters Set the standard for experimentation and causal inference across Model Experience, including where launches lack a clean A/B test, and personally lead the highest-stakes analyses Turn complex analyses of model and user behavior into clear narratives and recommendations that drive decisions with senior leadership and cross-functional partners Establish foundational data practices and shape the analytics infrastructure Model Experience needs to support rapid iteration and decision-making as our models and products evolve You may be a good fit if you: Have extensive experience in data science, including leading analytics for a product or research area at significant scale Have 3+ years managing data science teams, including setting team direction, hiring, and developing senior data scientists Have been a trusted partner to senior product, research, and business leaders, with clear examples of changing a roadmap or high-stakes decision Bring strong product sense and business judgment, including the ability to frame the right question and take a position with incomplete information Have deep expertise in experimental design, causal inference, and statistical modeling at high scale, with enough fluency in Python and SQL to stay hands-on and guide others' technical work Communicate clearly in writing and in presentations, including to executive audiences Have a bias for action and thrive in ambiguous, fast-moving environments where you must create clarity and drive progress Care about the company's mission of building helpful, honest, and harmless AI Strong candidates may also have: Experience with AI/ML products, large language models, or developer tools in the AI/ML ecosystem Experience as a tech lead manager or player-coach, balancing hands-on technical work with people management Experience building or scaling a data science team in a fast-growing organization Experience working closely with Product or Research teams, especially on LLM or AI products Comfort working with unstructured data The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $380,000 — $460,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.