Monte Carlo
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Operations
The Monte Carlo Operations Team is dedicated to building an exceptional employee experience that reflects our core values. Spanning across recruiting, people, and culture, our mission is to hire, retain, and empower the best global team to drive Monte Carlo’s vision of accelerating the world’s adoption of data. Our key focus areas include: Hiring the Best Global Team: Attracting top talent worldwide to join and thrive at Monte Carlo. People Success: Developing programs that boost engagement, retention, and performance, ensuring every team member has the support to excel. Culture & Engagement: Fostering a culture of inclusion and engagement where employees
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What People Are Saying About Monte Carlo
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Autonomy: Ops roles are portrayed as high-ownership with end-to-end responsibility, including practices like “Week 1 Ship” and a bias to make decisions quickly. Teams are expected to build systems and iterate without heavy handoffs or micromanagement.
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Low Bureaucracy: Operations is described as favoring speed and practical results over process for its own sake, with small, high-trust teams and “no committees.” This setup enables Ops to design and launch programs rapidly rather than function as administrative support.
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Team Support: Colleagues and managers are often characterized as accessible and collaborative, with cross-functional help, leadership AMAs, and regular syncs reinforcing connection. These norms help a remote-first Ops team stay aligned while solving problems together.
What People Are Saying About Monte Carlo
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Autonomy: Teams in San Francisco operate with high ownership and end-to-end accountability, aligned with norms like "measure in minutes" and "ship and iterate." Roles emphasize small, high-trust groups with minimal handoffs and a focus on output over process.
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Team Support: Colleagues in San Francisco are collaborative and accessible, aided by regular leadership AMAs, weekly syncs, and cross-functional touchpoints that keep a distributed team connected. Managers are described as supportive, with teams helping one another across functions.
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Innovation & Products: The San Francisco team shares a "ship before perfect" ethos, with company-wide sprints where employees rapidly built and shipped AI agents and openly discussed failures to accelerate learning. This experimentation mindset is reinforced by visible iteration rituals and a candid culture.
Recently posted jobs
Big Data • Cloud • Software • Generative AI • Big Data Analytics
Own and expand a portfolio of 15–25 enterprise accounts, generating approximately $1.2M in annual expansion quota. Prospect within existing customers, identify new departments and use cases, build executive relationships, run business and quarterly reviews, and close complex 6–12 month enterprise deals. Partner with sales engineers, customer success, and leadership while creating account plans, developing pipeline, and presenting Monte Carlo’s data and AI reliability platform to technical and executive buyers.
Big Data • Cloud • Software • Generative AI • Big Data Analytics
Leads Monte Carlo’s Enterprise Americas sales team, overseeing forecasts, pipeline coverage, new-logo bookings, outbound prospecting, deal inspection, and enterprise sales coaching. The role requires developing a repeatable sales process, coaching reps through complex executive-level deals, recruiting enterprise account executives, and partnering with sales engineering, SDR, marketing, and customer success teams. Candidates should have prior enterprise SaaS sales leadership experience and expertise in disciplined methodologies such as MEDDPICC and value-based selling.
Big Data • Cloud • Software • Generative AI • Big Data Analytics
Drive enterprise pipeline through outbound prospecting, lead qualification, stakeholder engagement, and meeting booking. Partner with Account Executives to execute outreach strategies, triage inbound leads, convert prospects into qualified opportunities, and refine the enterprise outbound playbook. Collaborate with Marketing on messaging, field feedback, and shared pipeline goals in a remote-first, early-stage environment.
