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The Director, UX Research owns the strategy, quality, and impact of user research across Okta's Design & UX Research organization. Reporting to the VP of Design & UX Research, you lead a team of researchers and own Research Operations as a distinct, self-contained function: the single, clear place researchers and their partners go for research infrastructure, panel/recruiting operations, tooling, and methodology standards.

This role requires equal parts research rigor and business instinct. You're comfortable defending a methodology choice to a skeptical PM, and just as comfortable killing a study that won't change a decision. You build a team and a research practice that changes what gets built.

You are accountable for embedding research in product decisions while they're still being made, so it shapes the spec instead of validating it after the fact. That means research capacity, prioritization, and synthesis have to move at the speed of the roadmap, and when they don't, you're the one who says so and fixes it.

A core part of this role is bringing AI-native ways of working to research itself: compressing the cycle from insight to decision through AI-assisted synthesis, rapid validation, and faster study turnaround, without trading away rigor. You are expected to lead from the front here.

• Set the research agenda: define what Okta needs to learn to win the largest enterprises and to make AI-powered identity trustworthy to admins, and build the multi-quarter research roadmap that gets us there, ahead of when Product asks for it.
• Prioritize for leverage: evaluate research requests with the same rigor you'd apply to your own hypotheses. Kill studies that won't change a decision, and protect capacity for the research that will.
• Own the insight pipeline: ensure research insights are logged, prioritized, and tracked all the way through to shipped outcomes, closing the loop between 'we learned this' and 'we changed that.'
• Represent research at the table: bring research evidence into planning and roadmap conversations as a forward-looking point of view that shapes what gets built.

• Build and lead the team: hire, manage, and grow a team of researchers and research managers who are craft experts and player-coaches, holding a high bar for rigor, judgment, and business impact alongside methods knowledge.
• Scale without bloat: build research infrastructure that scales with the org's growth (including Bangalore) without adding process for its own sake; actively retire rituals and templates that no longer earn their keep.
• Coach for judgment: develop researchers' ability to choose the right method for the decision at hand, defend their findings under pressure, and know when a good-enough signal fast beats a perfect signal late.

• Compress insight-to-decision cycle time: lead adoption of AI-assisted synthesis, transcription, and rapid analysis so findings reach decision-makers within days, and model this shift for the broader Design & Research org.
• Rethink validation for speed: build fast, rigorous validation methods (in-context testing, rapid usability passes, lightweight quant) that get real signal from real users quickly, and know when a fast rough study beats a slow perfect one.
• Evaluate AI research tooling critically: bring the same rigor to AI research tools that you'd bring to any method. Adopt what genuinely increases velocity or quality, reject what just looks innovative.

• Product & engineering alignment: partner with Product and Engineering leadership so research is built into planning at the start of major initiatives, while decisions are still open.
• Design partnership: partner tightly with Design leadership so research and design move in lockstep: research informs what gets designed, design partners on how it gets tested.
• Executive communication: present research findings and their business implications directly to leadership and, when relevant, to customers.

• Research shapes decisions early: research is present at the start of every major product initiative, and research citations show up in the decisions that follow.
• Insights ship: User Research Insights are logged, prioritized, and tracked through to shipped outcomes, with a visible, low rate of insights that get filed and never acted on.
• Usability and trust improve: usability benchmarks and practitioner trust in features improve measurably.
• Velocity without corner-cutting: insight-to-decision cycle time drops meaningfully through AI-native research practices, with no drop in rigor or defensibility of findings.
• A team that leads: researchers on your team come to cross-functional conversations with a prepared point of view and evidence.
• Research Ops: Research Operations runs as a self-sufficient function; recruiting, tooling, and methodology standards work smoothly

• Our north star is the real user experience in the real world: our job isn't done when we deliver a design or publish a report, it's done when something improves for users because of it.
• We advocate relentlessly for users: you never ask 'is this good enough?' You name the delta to excellence and bring the tradeoff to decision-makers with context.
• The faster we learn, the cheaper our mistakes: you default to the faster path to real signal, treat an invalidated hypothesis as a win, and don't let assumptions sit untested and compounding.
• We lead: you and your team come to the table with a point of view and evidence, influence the roadmap, and advocate for it without waiting to be asked.
• Clarity is kindness: you give direct, specific feedback on research quality and rigor, and you expect the same from your team.
• Raise your own bar: you ask 'what would great research look like here?' and hold your own team's craft to that answer.
• Move fast, simplify, repeat: you make informed calls on method and scope without waiting for full consensus or perfect data, and you remove research process that has stopped earning its keep.

• Master’s degree in a related field, or equivalent experience
• 10+ years of experience in user research, with 5+ years leading and developing research teams, ideally in enterprise B2B SaaS
• Demonstrated track record of research directly shaping product and roadmap decisions
• Deep fluency across qualitative and quantitative methods, with strong judgment on when to use which, and when 'fast and rough' beats 'slow and perfect'
• Experience owning or partnering closely with Research Operations (recruiting, panels, tooling, repository, compliance)
• Experience adopting AI-assisted research and synthesis workflows to compress cycle time without sacrificing rigor
• Strong executive presence: able to present findings and defend methodology directly to senior leadership and, at times, customers
• Demonstrated experience partnering with Product and Engineering leadership on cross-functional planning
• Proven ability to build, coach, and scale a research team, including developing player-coach managers
• High level of integrity, with the ability to handle sensitive customer and organizational information with discretion

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The annual base salary range for this position for candidates located in the San Francisco Bay area is between: $251,000 — $345,000 USD

Below is the annual base salary range for candidates located in California (excluding San Francisco Bay Area), Colorado, Illinois, New York, and Washington. Your actual base salary will depend on factors such as your skills, qualifications, experience, and work location. In addition, Okta offers equity (where applicable), bonus, and benefits, including health, dental and vision insurance, 401(k), flexible spending account, and paid leave (including PTO and parental leave) in accordance with our applicable plans and policies. To learn more about our Total Rewards program please visit: https://rewards.okta.com/us.

The annual base salary range for this position for candidates located in California (excluding San Francisco Bay Area), Colorado, Illinois, New York, and Washington is between: $224,000 — $308,000 USD.

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