Staff Data Scientist - Product Analytics
About The Position
As a Staff Product Analytics Data Scientist, you will be the analytical backbone of how Ironclad understands, measures, and improves its product. You'll turn raw product usage and contract data into a deep, quantitative understanding of how customers adopt our platform and our AI and you'll translate that understanding into decisions that shape the roadmap. This is a builder's role. Our team owns its own data end to end: we model and maintain our own dbt pipelines, mine large and messy datasets for signal, and partner closely with Product and Engineering to ship data products that put insight directly into customers' and teammates' hands. You'll wear a product analytics hat most days, an analytics engineering hat when the pipeline needs it, and a data science hat when a problem calls for experimentation, causal inference, or modeling. As a Staff-level individual contributor, you'll set analytical direction, raise the technical bar across the team, and influence senior stakeholders without formal authority.
Requirements
- 8+ years of experience in product analytics, data science, or a closely related quantitative field, including demonstrated impact at a senior or staff level (ideally at a B2B SaaS company).
- Deep expertise in product analytics: experimentation and A/B testing, funnel and retention analysis, causal inference, and defining product metrics that stand up to scrutiny.
- Advanced SQL, plus fluency in Python or R for analysis, statistics, and modeling.
- You can own dbt models, data modeling, and ELT best practices, and you're comfortable being the person who fixes the pipeline rather than filing a ticket.
- A track record of partnering with Product and Engineering to ship data products or data-informed features, not just deliver dashboards and reports.
- Strong data mining and exploratory instincts: you find the signal in large, messy datasets and know which findings are worth acting on.
- Experience (or strong interest) in AI-assisted development and "AI-ready" data—using tools like Cursor or Claude Code, and writing documentation and metadata that help humans and LLMs work with data reliably.
- Familiarity with a modern data stack such as Segment, Fivetran, BigQuery, dbt, Airflow, Looker, and exploration tools like Hex (or their equivalents).
- A self-starter who leads initiatives end to end, communicates with clarity, and bridges technical work and business impact.
Responsibilities
- 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.
Benefits
- 100% health coverage for employees (medical, dental, and vision), and 75% coverage for dependents with buy-up plan options available
- Market-leading leave policies, including gender-neutral parental leave and compassionate leave
- Family forming support through Maven for you and your partner
- Paid time off - take the time you need, when you need it
- Monthly stipends for wellbeing, hybrid work, and (if applicable) cell phone use
- Mental health support through Modern Health, including therapy, coaching, and digital tools
- Pre-tax commuter benefits (US Employees)
- 401(k) plan with Fidelity with employer match (US Employees)
- Regular team events to connect, recharge, and have fun
- The opportunity to help build the company you want to work at
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
