>_ Data Science and Agentic AI Leader
Ben Robinson
Data science. AI agents. Leading teams. Setting culture.
Applied ML at Google. Built on rigor, clarity, and intention.
>_ Data Science and Agentic AI Leader
Data science. AI agents. Leading teams. Setting culture.
Applied ML at Google. Built on rigor, clarity, and intention.
I lead a data science and AI team at Google focused on security and abuse prevention. My work centers on four things: doing data science with discipline, shipping AI agents that earn trust in production, leading teams that think clearly and ship reliably, and setting a culture where people feel psychologically safe, empowered to own their work, and trusted, with standards and feedback explicit, not assumed.
I stay close to the work (code, evaluation frameworks, and modeling decisions) while developing senior ICs into leads who own their domains. I care as much about how we decide what to build and how we review it as I do about what we ship. The goal is not volume; it's impact through intention.
I've worked across adtech, healthcare, media, and big tech. The constant: rigorous analysis, production-grade ML, and a preference for clarity over spectacle.
Rigorous, production-oriented data science: causal inference, experimentation, and measurement so that decisions are grounded in evidence. The discipline is in how we define the question, choose the method, and interpret the result, not in chasing the latest technique.
Designing and shipping AI agents that run in production, with clear evaluation, guardrails, and human-in-the-loop where it matters. The focus is on systems that automate real workflows reliably, not demos. From concept to deployment, with standards that stick.
Building and leading data science teams that ship. Hiring for rigor and judgment, developing ICs into leads who own their domains, and maintaining clear roadmaps and cross-functional alignment. I want people to feel trusted and empowered, so they can take ownership, disagree when it matters, and deliver consistently.
Making how we work explicit: modeling standards, review practices, and expectations for quality and communication. I care that the team is psychologically safe, that people can disagree, take ownership, and be trusted with hard problems. Culture is what happens when you're not in the room; I invest in written norms, feedback loops, and a default toward transparency and empowerment.
ML-driven customer classification to identify creditworthy GCP customers. Shortened the identification window from 60 days to 3, with clear evaluation and alignment with Finance and Risk. Example of data science rigor applied to a high-stakes business decision.
Led launch of an AI agent that automates quota allocation decisions, replacing a manual, vendor-dependent process. Defined evaluation and guardrails before rollout; eliminated vendor OpEx and reduced on-call burden by 90%. Team ownership and culture of “measure before we scale” was key.
Partnered with Product to redesign quota tier structures using ML-driven controls. 85% reduction in fraud rate without impacting legitimate users. Required tight coordination across modeling, policy, and product, and a shared bar for what “right” looks like.
Led five data science projects delivering $9.5M in annualized medical claim cost reductions for Medicare. Production ML pipelines and causal inference within strict compliance. Tech lead for a team of 4; presented to senior leadership including the Chief Actuary. Set expectations for rigor and communication early.
Data science and leadership across industries. The through-line: rigor, clarity, and intention.
Lead a data science and AI team of 8 across customer growth, revenue, and access controls for Google Cloud. Own roadmap, modeling standards, and cross-functional alignment with Product, Engineering, Finance, and Risk. Prioritize a culture where people are psychologically safe, empowered to own their domain, and trusted, with clear standards, review practices, and feedback as the norm.
ML-driven controls for GCP products; partnered with Product and Engineering to manage risk at scale. Introduced causal inference as a team standard, the kind of discipline that later made “how we evaluate” part of team culture.
Aetna (CVS Health)
Production ML and causal inference for Aetna Medicare, within strict compliance. Tech lead for 4 data scientists; set expectations for rigor and how we communicated with leadership.
Dotdash
Content recommendation and monetization: seasonality and text-similarity modeling at scale. Cross-functional work with Product and Ad Ops during a volatile period; focus on stable, interpretable results.
AppNexus
Revenue prediction and distributed pipelines for real-time bidding and programmatic advertising. Data science in a high-throughput, production environment.
City College of New York
Washington University in St. Louis
Collaboration, questions, or a conversation about data science, AI agents, or building teams and culture. Please reach out.