About the role
About
the Role We’re hiring a Data Scientist to support our Marketing Innovation pod, a cross-functional team building the internal tools and agentic systems that fundamentally change how we do marketing and serve customers. We build product-like systems that: Deliver high-touch, consultative experiences to millions of SMB customers through agentic lifecycle and sales experiences Adapt messaging, creative, and outreach using real-time behavioral signals Power intelligent routing, targeting, and engagement decisions at scale, with minimal human-in-the-loop In this role, you’ll be embedded with Product and Engineering to ensure these systems drive measurable business outcomes. What You’ll Do Define success metrics for agentic marketing systems (e.g., incremental pipeline generated, conversion lift, rep hours saved), including leading indicators that enable weekly iteration. Design measurement and experimentation frameworks for always-on systems across lifecycle automation, creative generation, targeting, and routing — using holdouts, staged rollouts, and quasi-experimental methods when needed. Partner with PMs and engineers to instrument, evaluate, and monitor launches so every meaningful release has observability and a credible read on incremental value. Translate behavioral and model-driven signals into decisions: what to scale, where to intervene, and how to allocate human and compute attention across segments. Build repeatable decision loops (pre-launch criteria → post-launch read → next action) that convert analysis into shipped changes. What We’re Looking For 10+ years in a quantitative role (e.g., Data Science, Decision Science), ideally at a product-led company supporting B2B growth, with exposure to SMB or scaled self-serve motions. Deep grounding in experimentation, causal inference, and applied statistics, with experience designing and interpreting tests in real-world, always-on environments. Strong technical fluency in SQL and Python, including working directly with messy, incomplete behavioral data to quantify impact. Proven track record of translating results into shipped decisions (product, lifecycle, targeting, routing). Strong business judgment and a bias toward action: able to scope ambiguous problems, define success, and move quickly from insight to strategy. Excellent communicator and partner to PMs/Engineers; comfortable influencing stakeholders and presenting recommendations to senior leadership.
Amazon