Machine Learning Scientist 5 - Sales, Ads DSE

As the ML lead for Sales productivity and automation, you'll own the technical vision and execution across areas such as: Workflow Automation: In partnership with Product, identify and automate high-friction points in the Sales workflow to reduce delays, eliminate manual work, and improve accuracy — so sellers spend less time on process and more time on selling. AI-Augmented Selling: Design and deploy AI agents and assistants that make selling faster and more effective — automating where possible and helping sellers prepare for client conversations, respond to RFPs, and close higher-value deals more quickly. Define the AI/ML investment roadmap for the Sales pod in close partnership with product and engineering, identifying where data science can drive the most meaningful improvements in Sales productivity and revenue outcomes. Design, build, and deploy production ML models, AI agents, and automation systems end-to-end. Partner with Analytics Engineers to build the metrics, measurement pipelines, and reporting that guide investment decisions and track product performance. Iterate on launched products based on performance data and seller feedback, with a clear eye on measurable business impact. Serve as a senior technical voice in cross-functional discussions, communicating clearly with both technical and non-technical stakeholders. Advanced degree (PhD or Master's) in Computer Science, Statistics, Mathematics, or a related quantitative field. Track record of independently scoping and leading end-to-end ML projects in production environments — not just executing on defined projects, but identifying the right problems to solve and driving them from idea to measurable impact. Strong strategic acumen: the ability to understand business objectives deeply and translate them into a prioritized technical roadmap. Comfort with ambiguity and a proactive, curious approach to problem-solving — you can operate effectively in a space where the path isn't always laid out for you. Excellent communication skills with both technical and non-technical audiences, paired with the curiosity and user empathy to understand Sales pain points and needs. Proficiency in Python and SQL for building ML pipelines and models. Experience with LLMs, AI, or agentic systems is a bonus, but not required, given how new these fields are - we think candidates with strong ML product-building experience willing to learn into AI can also be highly successful in this role.