Deployment Strategist

ABOUT READYON

ReadyOn is an AI-native Labor Operating System redefining how the world's largest enterprises manage frontline labor. Born out of Stanford University, the company applies advanced AI and market-design principles to match the world's 2.7 billion frontline workers to the right shifts, in real time.

The platform is already proven at global scale, powering labor operations for several of the world’s largest enterprises. Landmark customers include an F250 food-service enterprise (300K employees across 16 countries; $7B+ annual labor spend), an F500 hotel group (250K+ employees; $5B+ annual labor spend), and an F250 entertainment operator (75K employees; $4B+ annual labor spend). ReadyOn reshapes how enterprises think about workforce design and management, with an AI system of action that transforms labor from a fixed cost into a strategic advantage.

The founding team bridges economics, AI, and enterprise operations -- Dominic Mirabile (Stanford GSB, ex-Deloitte), Mohammad Akbarpour (tenured Stanford economist), and Reza Iranmanesh (ex-Google Cloud). ReadyOn raised a $22M Series A led by Venrock and is advised by Nobel Laureate Paul Milgrom and former Microsoft Chief Economist Susan Athey. Headquartered in San Francisco with ~90 employees, 8x YoY revenue growth.

THE ROLE

Deployment Strategists embed across ReadyOn's Enterprise Partnerships team to own the analytical and operational work that turns complex labor environments into working product deployments. You will contribute directly to live F500 implementations -- building configurations, running data analyses, supporting training delivery, and participating in customer engagements -- while developing the structured product intelligence that helps engineering and product teams understand what's actually happening in the field.

You’ll work across multiple accounts and Engagement Managers, and operate at the intersection of customer implementation and product development. The best people in this role become the team's institutional memory -- the person who has seen enough deployments to know what patterns to surface, what configurations fail silently, and what product gaps are costing the team time and customer outcomes.

The role is anchored in deployment execution early on, with growing scope into product synthesis, analytical infrastructure, and solutions work as you build platform depth. Strong performers grow into Engagement Management, Solutions Engineering, or Product roles.

What this role offers:

• Direct contribution to live F500 deployments from day one
• Exposure to the full deployment lifecycle across Fortune 500 accounts: configuration, data analysis, training delivery, and go-live execution
• A voice in product direction, backed by the deployment data and analysis you produce.
• Opportunities to define your own area of expertise in a fast-paced team environment
• Meaningful impact on how hundreds of thousands of frontline workers experience their daily work

RESPONSIBILITIES

• Execution of Client Deployments -- Own defined deployment workstreams, including go-live preparation, configuration troubleshooting, and manager training. Lead relevant client calls and provide technical and analytical depth in customer conversations.
• Configuration & Solution Design -- Translate employer data and rules into system-ready configurations. Own solution design details in pilots and early-stage deployments; contribute to validation and UAT testing.
• Product Synthesis -- Surface field-level product gaps to Product and Engineering grounded in deployment data. Write feature requests informed by cross-account pattern analysis.
• Data Analysis & Reporting -- Build deployment health dashboards, cross-customer operational analyses, and adoption/usage metrics. Run ad-hoc data pulls for customer-facing deliverables (board decks, QBR materials). Provide the analytical foundation for the team's labor optimization mandate.
• Automation & Leverage -- Build reusable templates, scripts, playbooks, and analytical frameworks that make each subsequent deployment faster. Support data warehouse and internal analytics infrastructure as it develops.

EXPERIENCES AND SKILLS

Analytical Problem-Solving & Data Fluency

• Breaks down ambiguous operational questions into structured analytical approaches
• Comfortable working with raw data -- exports, API responses, unfamiliar schemas -- and cleaning it up into something usable across the team
• Identifies patterns across multiple data sets or engagements
• Moves from data to "so what" quickly -- analysis that ends in a recommendation

Technical Orientation & Systems Thinking

• Picks up new tools, schemas, and technical environments quickly
• Thinks in systems: comfortable modeling data relationships, tracing how configurations create downstream outcomes, and engaging with engineers on technical questions
• Basic comfort with code: can read scripts, automate repetitive tasks, and debug configurations without handing off every technical problem
• Familiar enough with integration concepts -- APIs, data sync, webhooks -- to diagnose problems and communicate clearly with engineers and non-technical managers

Execution & Operational Rigor

• Takes ownership of multiple concurrent workstreams, identifies what needs to happen next, and follows through without close supervision
• Builds repeatable processes that improve team effectiveness and execution consistency
• Biases toward action and pragmatic solutions; comfortable translating ambiguity into action
• High learning slope and comfort operating in a fast-moving environment

Communication & Collaboration

• Distills complex information clearly and communicates with polish in client calls
• Turns deployment observations into useful, structured product feedback
• Clear writer -- summaries of analyses, feature requests, and status updates that stand on their own
• Works well cross-functionally across engineering, product, and customer teams

Nice-to-have:

• Python proficiency or experience with data analysis or BI tools
• Familiarity with AI-assisted development tools (Claude Code, Cursor)
• Exposure to enterprise SaaS, workforce management, or labor operations

~0-3 years of professional experience. Degree in Computer Science, Economics, Business, Data Analytics, Mathematics, Engineering, or similar analytical field.

Location: San Francisco, CA (onsite).

The salary range for this position is $95,000-$150,000. Compensation is determined based on a variety of factors, including an individual's skills, experience, and qualifications.

We encourage all candidates to apply even if your experience doesn't exactly match our job description. We are committed to building a diverse and inclusive workplace where everyone (regardless of age, religion, ethnicity, gender, sexual orientation, and more) feels like they belong. We look forward to hearing from you!