Brand Insights, Measurement Lead

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 a Brand Insights, Measurement Lead at Anthropic, you will strengthen the evidence that fuels our brand decisions: knowing whether marketing spend and effort is working, saying so clearly on a schedule to the people who need to hear it. Marketing Insights turns market, campaign and user data into decisions that product, marketing, comms and sales leadership can act on. You'll tell us, with data, whether awareness is turning into consideration, whether consideration is turning into pipeline, and where that chain breaks down for a given audience. This role sits at the intersection of human understanding, marketing science discipline and organizational influence. In this role, you'll own the team's recurring measurement readouts, including the Prosumer and B2B brand health tracker studies and the metrics, headwinds and tailwinds that show up in front of business leadership and in all-hands updates, keeping them accurate, current and easy to act on. You'll partner closely with your Marketing Insights teammates, Comms and data science on the brand-health dashboard, Brand & Integrated Marketing leadership, brand data science, research vendors, and cross-functional stakeholders across Enterprise Marketing, product marketing and finance. Responsibilities Own the recurring executive and all-hands readouts that put key metrics, headwinds and tailwinds in front of marketing management and the broader team Lead process ownership of the Prosumer and B2B brand health tracker studies, including managing the research vendor relationships that field them Lead coordination across Comms, Marketing and data science on the brand-health dashboard, aligning all three groups on one consistent view of the data Support media mix modeling, multi-touch attribution and incrementality analyses that inform how marketing budget and effort get allocated, moving the team past correlation to a defensible read on what's actually driving outcomes Run best-practice pre- and post-campaign analysis on individual campaigns, covering creative, channel, audience, and timing effects, and turn each read into a specific change for the next campaign Roll up campaign-level learnings into a recurring view for leadership: which channels, creative approaches, and audiences are outperforming over time, and where the team should adjust the playbook next Build audience and funnel analyses that quantify how different audiences move, and relate to each other, from awareness through consideration to conversion, and what that means for where to invest Audit how customer sentiment gets reported across the business and design a more efficient, consistent process Manage the underlying data pipelines so that recurring readouts stay reliable and don't get rebuilt from scratch each cycle Partner with Marketing Insights teammates to keep measurement process and research design in sync, including feeding into the Marketing Business Review scorecard process Develop one defensible view from first-party behavioral data, campaign and media data, and survey or attitudinal research, being clear about what each source can and can't tell you on its own Present findings to non-technical audiences, including marketing leadership, enterprise sales and finance partners, translating hedged results into clear recommendations You may be a good fit if you Have a strategic orientation, strong curiosity for understanding what customers and users think and how to shift perceptions, and can use that to influence business and marketing direction Are fluent in SQL and Python or R, and move comfortably between a quick exploratory pull and a more rigorous statistical model Have designed or analyzed experiments (A/B tests, holdouts, lift tests) to establish a causal effect rather than settle for a correlation, and have applied regression, segmentation or other causal-inference methods to a real business question Have run campaign-level measurement end to e #J-18808-Ljbffr