Deployment Strategist

Deployment Strategist Role Joining the Enterprise Partnerships team, the Deployment Strategist works across multiple customer deployments and alongside our Engagement Managers to turn complex labor environments into working ReadyOn deployments. You will contribute directly to live Fortune 500 implementations: building configurations, running data analyses, supporting training delivery, and joining customer engagements. You will also build the structured product intelligence that helps Engineering and Product understand what is happening in the field. The best people in this role become the team's institutional memory, and the role grows from deployment execution into product synthesis, analytical infrastructure, and solutions work. Strong performers move into Engagement Management, Solutions Engineering, or Product roles. Ideal Candidates: • Learn by Doing: You get up to speed quickly in unfamiliar tools, data, and operating environments, and you are comfortable contributing to live Fortune 500 deployments from day one. • Turn Data into Decisions: You break down ambiguous operational questions, work directly with messy data, and move quickly from analysis to a clear recommendation. • Own Your Workstreams: You take ownership of multiple workstreams at once, work out what needs to happen next, and follow through without close supervision. • Build for Reuse: You look for the template, script, or playbook that makes the next deployment faster than the last, and you build it. • Be the Bridge: You explain technical and analytical findings clearly to engineers, product managers, and non-technical operators, and you turn what you see in the field into structured product feedback. Responsibilities & Impact • Execute Client Deployments: Own defined deployment workstreams, including go-live preparation, configuration troubleshooting, and manager training. Lead relevant client calls and bring technical and analytical depth to customer conversations. • Design Configurations and Solutions: Translate employer data and rules into system-ready configurations. Own solution design details in pilots and early-stage deployments, and contribute to validation and UAT testing. • Turn Field Insight into Product Direction: Surface field-level product gaps to Product and Engineering, grounded in deployment data. Write feature requests informed by cross-customer pattern analysis. • Deliver Data Analysis and Reporting: Build deployment health dashboards, cross-customer operational analyses, and adoption and usage metrics. Run ad hoc data pulls for customer-facing deliverables such as board decks and QBR materials. • Automate and Reuse: Build reusable templates, scripts, playbooks, and analytical frameworks that make each subsequent deployment faster, and support the data warehouse and internal analytics infrastructure as it develops. Your Background • 0-3 years of professional experience, with a degree in Computer Science, Economics, Business, Data Analytics, Mathematics, Engineering, or a similar analytical field. • Analytical Problem-Solving and Data Fluency: You structure ambiguous operational questions into analyses, work comfortably with raw data (exports, API responses, unfamiliar schemas), and spot patterns across data sets and engagements. • Technical Orientation: You pick up new tools and schemas quickly, think in systems, and have basic comfort with code: you can read scripts, automate repetitive tasks, and debug configurations. You know enough about APIs, data sync, and webhooks to diagnose problems and talk with engineers. • Execution and Operational Rigor: You manage several concurrent workstreams, follow through without close supervision, and build repeatable processes that make the team more consistent. • Internal and External Communication: You distill complex information clearly on client calls and write summaries, feature requests, and status updates that stand on their own. You work well across engineering, product, and customer teams. • AI-assisted development tools: Claude Code, Cursor, nice to have: Python or BI tool experience • Nice to Have: Exposure to enterprise SaaS, workforce management, or labor operations.