Sr. Analyst, Applied AI (G&A Intelligence)

OpenGov•San Francisco, CA

1d•Hybrid

About The Position

OpenGov is seeking a Sr. AI Analyst, People Intelligence to serve as the dedicated practitioner embedded within OpenGov’s growing Applied AI/innovation team, focused on G&A use cases. This role sits at the intersection of messy data, workflow automation, and applied AI — and exists to systematically redesign how OpenGov’s HR, Talent Acquisition, and Enablement functions operate in an AI-native world. You will identify, prioritize, and build AI-powered workflows, tools, and copilots that reduce friction, improve decisions, and free up time for the work that actually requires human judgment. This is an analyst and workflow automation role first, not a software engineering role. You bring a sharp process mind, comfort with modern AI tools (e.g. Claude/ChatGPT, 3rd party AI tools, and no-code/low-code automation platforms), and the instinct to translate a messy, manual workflow into something smarter. You don’t need to write production code, or be an HR Ops/Tech specialist. What you DO need is to be a problem-solver with the ability to deeply understand how your partner teams actually work, identify where AI-native redesign creates the most leverage, and build or orchestrate solutions that real people will use to solve real pain points. You will work within the Applied AI team’s technical ecosystem and governance framework, owning the full problem-to-solution lifecycle yourself, and partnering with AI/data engineers when needed. Given the sensitivity of the data, this role requires a mature, genuine understanding of data confidentiality and HR compliance. You will have access to data and systems at a level comparable to senior HR leadership, and you will be expected to exercise that access with the discretion and judgment that earns it. While this role sits within the Applied AI team, you will maintain a close working relationship with the HR Technology Leader, who will provide domain guidance on HR systems, compliance context, and functional priorities — ensuring your work is grounded in how the People org actually operates.

Requirements

Bachelor’s degree in HR Tech, Information Systems, Organizational Psychology, or Data Analytics, or a related field.
4–7 years of experience in an individual contributor (IC) role in technical product management, data science, people analytics, or a closely adjacent function. We need someone who has been hands-on, on the ground, building and improving things.
Demonstrated experience using AI tools in a professional context — not just familiarity. You have been actively using tools like Claude or ChatGPT, as well as agentic orchestration systems like n8n, make.com, or Airia, to meaningfully improve how you or your team works, and you can speak concretely about what you’ve built or changed as a result.
Strong process analysis and workflow redesign skills: you can map a messy current-state process, identify where AI creates leverage versus where it creates risk, and design a future-state that people will actually adopt.
Comfort working with no-code and low-code automation tools (e.g., Zapier, Make, Workato, or equivalent) and enterprise SaaS platforms (Workday, Ashby, or similar HR systems).
A mature, demonstrated understanding of HR data sensitivity and employment law constraints — including what data can and cannot be used to make automated decisions, what requires human review, and how to design systems that protect employee privacy without sacrificing usefulness.
Strong analytical skills: comfortable with data in spreadsheets, dashboards, or basic BI tools (Tableau, Looker, or similar); able to extract insight from People data and frame it clearly for both technical and non-technical audiences.
Exceptional written and verbal communication: For example, you can explain a complex AI workflow to an HR business partner, the tradeoffs of a data access decision to a legal stakeholder, and the technical requirements to an AI engineer. You write clearly and you don’t create information silos.
Self-starter with high initiative: you see opportunities, you propose solutions, and you drive them forward without waiting to be told. This role will involve significant ambiguity, especially early on, and the right person will find that energizing rather than paralyzing.
Collaborative by default: you build trust with domain experts before designing for them, you surface tradeoffs clearly, and you understand that the best AI workflow in the world fails if the people it’s designed for don’t believe in it.

Nice To Haves

Familiarity with AI governance concepts: prompt design, data access controls, audit trails, and responsible AI principles in the context of HR applications.
Experience building or deploying Slack-based bots, workflow automations, or internal tools without engineering support.
Prior experience in an HR technology, people analytics, or HR ops role at a SaaS company or in a compliance-intensive environment.
Exposure to employment law, I-9/EEO compliance, or compensation equity frameworks — not as a legal expert, but as someone who understands why these guardrails exist and how to design around them responsibly.
Experience working at the intersection of People and IT or People and Finance — roles that required navigating cross-functional data ownership and system permissions.
Familiarity with Snowflake, Looker, or similar data platforms at a read/analysis level (not engineering).
Background in organizational design, change management, or workforce transformation initiatives.
Experience working in a fast-moving, high-growth SaaS environment where the org is actively evolving and priorities shift quickly.
Prior exposure to government, regulated industries, or other compliance-heavy environments where data handling standards are elevated.

Responsibilities

Map and redesign People org workflows with an AI-native lens: go deep with HR, Talent, and Enablement teams to understand current-state processes, identify automation and AI augmentation opportunities, and translate findings into prioritized, sequenced action plans that account for effort, impact, and compliance sensitivity.
Build and deploy AI-powered tools and automations for the People organization: this includes manager copilots, candidate screening workflows, onboarding automation, policy Q&A assistants, enablement content tools, and other solutions using Claude, Slack, and connected enterprise platforms — operating within OpenGov’s AI governance framework.
Partner with AI, systems, or data engineers when required — clearly defining requirements, translating business needs into actionable technical specs, and owning outcomes on the People side while the engineering team handles backend complexity.
Serve as the primary AI capability advisor for the People org: help team leaders understand what’s possible with AI today, evaluate build vs. buy tradeoffs, identify where AI should not be applied (due to compliance or sensitivity constraints), and set realistic expectations on scope and timeline.
Own data sensitivity and compliance judgment as a core function of the role: every workflow you design or tool you build must account for the confidentiality of HR data, applicable employment law constraints, and OpenGov’s internal data governance rules. Flag risks proactively — don’t wait to be asked.
Maintain a living roadmap of AI initiatives across the People org: track ideas, in-flight projects, and delivered solutions; report on impact; and continuously reprioritize based on business need and resource availability.
Drive adoption and change management for AI tools you build: shipping is not the finish line. You own ensuring that the tools and workflows you create are understood, used, and refined based on real feedback from the people who depend on them.
Contribute to OpenGov’s broader AI flywheel: insights and workflow patterns developed inside the People org may inform how OpenGov advises government customers on people operations and workforce AI — you should document learnings in ways that can be reused.
Communicate progress, priorities, and blockers clearly across stakeholders: written updates, structured check-ins with People leadership and Applied AI leadership, and crisp escalation when decisions require broader alignment.