layiq

/la·yiq/adjective

worthy; deserving; fitting; suitable.

A role, opportunity, or path that merits attention, time, and pursuit.

Staff Data Scientist - Product Analytics at Ironclad | LAYIQ

Own product understanding.
Define, instrument, and analyze the metrics that describe how customers adopt, retain, and get value from Ironclad—funnels, activation, feature adoption (including our AI features like Jurist), engagement, and retention—and make them trustworthy and self-serve.
Drive experimentation and causal analysis.
Design and analyze A/B tests and quasi-experiments; apply causal inference where clean experiments aren't possible; and give Product and Engineering clear, defensible reads on what actually moved the needle.
Mine data for opportunity.
Explore large, complex product and contract datasets to surface non-obvious patterns, quantify opportunities, and generate hypotheses that shape strategy—not just answer questions that were already asked.
Build and evolve data products.
Partner with Product and Engineering to turn analysis into shipped features—embedded analytics, benchmarks, insights, and AI-powered experiences—that deliver value directly to customers and internal teams.
Wear the analytics engineering hat.
Own and extend the dbt models and transformations that power your work. Because our team owns its pipelines, you'll design scalable, well-documented, well-tested data models and uphold consistent definitions across the warehouse and BI layer.
Architect AI-ready data.
Leverage AI to accelerate your own pipeline and analysis work, and structure our data assets, documentation, and metadata so that both humans and LLMs can navigate them with high confidence and minimal hallucination.
Set the bar and mentor.
Provide technical direction, code and analysis reviews, and mentorship to analysts, data scientists, and analytics engineers—fostering a culture of rigor, collaboration, and impact.
Influence senior stakeholders.
Bring clarity to ambiguous, high-stakes product questions and communicate findings in a way that drives alignment and action across Product, Engineering, and leadership.
Self-serve enablement: Design and scale self-serve analytics ecosystems, semantic layers, and clear data documentation that empower non-technical stakeholders to answer their own data questions with confidence.

Staff Data Scientist - Product Analytics

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