Data Operations Manager, Scientific Data

Own the operations that turn Axiom's scientific plans into massive, high-quality experimental datasets that power frontier agents and models to predict human drug outcomes with insane accuracy. What you will do: • Own operational delivery of Axiom's scientific data programs, translating research and customer requirements into clear plans, milestones, quality standards, budgets, and deliverables. • Coordinate data generation across internal teams and partner laboratories, managing capacity, dependencies, timelines, and handoffs from experimental planning through validated data delivery. • Manage compound sourcing, purchasing, sample manifests, inventory, international shipping, and customs documentation so laboratories have the right materials when they need them. • Build and manage laboratory and supplier partnerships, establishing clear specifications, delivery commitments, acceptance criteria, and processes for resolving failures. • Protect data integrity by reconciling compound identities, barcodes, sample histories, experimental metadata, and incoming results, working with scientists and data engineers to resolve discrepancies before downstream use. • Build scalable operating systems using trackers, dashboards, SOPs, alerts, and AI agents to automate coordination, surface risks early, and reduce manual follow-up. • Keep pharmaceutical customers, AI research partners, laboratories, and internal teams aligned through clear communication, timely escalation, and reliable delivery. • Manage SOWs, purchase orders, invoices, and project budgets, tracking spending against progress and identifying opportunities to improve cost and turnaround. • Measure and improve throughput, delivery reliability, and data quality, ensuring each program strengthens the operations behind Axiom's drug discovery, Translate, and AI training workflows. What we look for: • Exceptional execution and end-to-end ownership. You anticipate blockers, manage competing priorities, and deliver complex programs across multiple teams and external partners. • Exacting attention to detail. You catch inconsistencies in samples, identifiers, datasets, schedules, and budgets before they become costly downstream failures. • Strong analytical and technical judgment. You use data, spreadsheets, SQL, Python, or other tools to investigate problems and make sound operational decisions. • A track record of building scalable processes. You turn recurring problems into clear workflows, automated checks, and systems that improve speed and reliability. • The ability to translate nuanced scientific and research requirements into concrete specifications, quality standards, and deliverables. • Strong partner management and communication skills. You build trust, set clear expectations, navigate difficult conversations, and keep customers, researchers, and vendors aligned under pressure. • Comfort with ambiguity and changing priorities, supported by experience in operations, program management, consulting, or similarly demanding roles. • Curiosity about biology, chemistry, drug discovery, and AI, with an interest in how high-quality experimental data improves models and scientific agents. • A habit of using AI tools and automation to improve your own work, with the judgment to verify outputs and maintain accountability. Technical skills we value: • Data management and analysis using spreadsheets, SQL, Python, dashboards, and automated reconciliation and validation tools. • Program planning, dependency tracking, capacity planning, risk management, and quantitative process improvement. • Workflow automation and AI agents for tracking deliverables, reconciling records, coordinating handoffs, and surfacing exceptions. • Vendor and commercial operations, including SOWs, purchase orders, invoices, budgets, and performance tracking. • Compound procurement, supplier coordination, inventory management, international shipping, and customs documentation. • Scientific operations, including experiment scheduling, sample tracking, compound identity management, and laboratory data delivery. • Clear SOPs, documentation, acceptance criteria, and escalation processes that keep internal teams and external partners aligned. • Prior biotech or laboratory experience is helpful but not required.