About the role

In support of Workato’s broader push toward becoming a wall‑to‑wall agentic company, one important area where this comes to life is the team’s investment in internal, agent‑powered systems, such as a User Insight Agent (PM Agent) that helps centralize feedback and package it into decision‑ready outputs. You’ll contribute to and benefit from that ecosystem, but the core of the role is broader: building a repeatable, modern “Insight Engine” for Product Research—powered by strong analytics, LLMs, and pragmatic automation—so insights land and drive measurable impact.

Product Research needs a next‑gen Product Analyst who can:

- Synthesize qual + quant across product verticals to inform product and design decisions.

- Go beyond “insight generation” to also improve insight transmission, making insights easier to consume (briefs, artifacts, decision narratives) and more likely to be acted on.

- Build automations and agents that increase the day‑to‑day efficiency of the Research and Design team, so the team can move faster without sacrificing rigor.

What you’ll do

1) Multimodal analysis for product & design decisions (quant + qual)

- Work with qualitative and quantitative data sources to consolidate product signals across channels.

- Partner closely with Product Researchers, Designers, and Product Managers to define schemas and pipelines that make qualitative and quantitative signals joinable (so we can connect themes to outcomes like churn, adoption, or task success).

- Run analyses that inform product and design decisions: trend analysis, segmentation, lightweight experimentation readouts, measurement strategy, and narrative synthesis.

- Use LLMs to help turn messy qualitative data into structured, explainable representations.

- Build small tools/dashboards that keep decisions data‑informed.

- Help teams frame hypotheses, interpret results, and connect insights to product/design actions across Workato’s product offerings.

2) Agentic insight delivery (insights that actually land)

- Build and iterate on a User Insight Agent that supports everyday product decisions

- Create stakeholder‑ready outputs with traceable evidence, tailored to the decision being made.

- Design for trust and reliability: transparency, citations to source evidence, confidence/uncertainty signals, rigorous evaluation/guardrails, and human‑in‑the‑loop controls.

- Implement evaluation + observability so the system improves over time (quality checks, sampling workflows, lightweight ground truthing, telemetry, and regression-style checks as the system evolves).

3) Research & design team automations (increase team velocity)

- Create automations/agents that streamline daily operations for Research and Design, aiming to make the team faster and more effective across recruiting, study operations, synthesis workflows, and knowledge management.

- Treat internal ops as a product: measure time saved, throughput gains, and quality improvements.

- Alpha-test the Workato Agentic platform and Enterprise MCP capabilities and provide concrete feedback to Product/Design

You’ll thrive here if you have

Must‑haves

- Strong AI use encouraged: you’re excited to use AI tools to move faster (while staying rigorous about correctness, privacy, and safety).

- Modern analytics skillset: strong SQL + data wrangling; comfortable translating messy questions into clean analysis.

- Text/qual comfort: excited to analyze and structure qualitative data (transcripts, notes, open‑ended feedback) alongside quantitative signals.

- LLM + agentic systems fundamentals: familiarity with prompting, structured outputs, tool/function calling, retrieval/RAG, and basic evaluation/guardrails.

- Builder mindset: you ship usable things: prototypes, automations, scripts, lightweight internal tools; not just slides.

- Product sense: you can clearly articulate tradeoffs, propose practical workflows, and align outputs to stakeholder decisions.

- Clear communication: you can make complex findings and systems legible through crisp writing, visualizations, docs, and demos.

Nice‑to‑haves

- Familiarity with MCP (Model Context Protocol) or agent frameworks; observability/eval tooling for LLM systems.

- Experience joining qualitative research artifacts with product telemetry in a principled way.

- Background in experiment design, causal thinking, or measurement strategy.

- Experience working in a SaaS environment or with enterprise customers.

- Ability to build lightweight UIs (e.g., TypeScript/React) for internal tools.

- Reliability mindset (testing, monitoring, safe iteration) for data/LLM workflows.

Tools & stack

- Python (analysis + scripting/services), SQL, notebooks, lightweight frontends

- LLM tooling: structured outputs, RAG/vector retrieval, eval/guardrails, tracing/telemetry

- Workato Agentic + Enterprise MCP platform

- Optional: Node.js/TypeScript, React

What you’ll gain

- A front‑row seat and real ownership on what “next‑gen analytics” looks like in an agentic company.

- Mentorship across Product Research, Design, PM, Engineering, and AI Lab teams.

- The chance to ship state-of-the-art Agentic products—both internally, and externally to customers—that influence real product decisions and help shape how insights flow through the org.

- Hands‑on experience building, evaluating, and hardening LLM/agent workflows for real stakeholders and real decisions.

- A stronger portfolio of practical work artifacts (insight narratives, lightweight tools, automations, evaluation setups) you can talk about.

Eligibility for application

- Graduating senior with a relevant BS/MS degree (CS, Data Science, HCI, Design Engineering, or related).

- Junior candidates with <2 years of experience and strong relevant projects may also be considered.

To stand out in our hiring process, please take the time to respond to the Job Application Questions below with concise yet informative answers. All submissions are personally reviewed by the Hiring Team, not evaluated by AI.

(REQ ID: 2536)