Staff Data Scientist

About the role

Ironclad is seeking a Staff Product Analytics Data Scientist to serve as the analytical backbone for how the company understands, measures, and improves its product adoption and usage. In this role, you will own the end-to-end analytical lifecycle for product and contract data, transforming raw information into a deep quantitative understanding of customer behavior. You will define, instrument, and analyze the metrics that drive insight into how customers adopt the platform and utilize its AI features such as Jurist. This is a builder-focused position where you will wear multiple hats, functioning as a product analyst, analytics engineer, and data scientist depending on the problem at hand. As a Staff-level individual contributor, you will set the analytical direction for the organization and elevate the technical rigor across the broader team. You will partner closely with Product and Engineering to ensure that insight leads directly to action, shaping the roadmap through data-driven decisions. Ultimately, you will be responsible for ensuring that Ironclad's data becomes a trusted, self-serve asset that empowers both internal stakeholders and external customers to make confident decisions.

Key facts

What you'll do

• Own product understanding by defining, instrumenting, and analyzing metrics that capture customer adoption, retention, and value realization across the entire Ironclad platform, including AI features like Jurist, ensuring these metrics are trustworthy and self-serve.
• Drive experimentation and causal analysis by designing and analyzing A/B tests and quasi-experiments, applying causal inference methodologies when randomization is not feasible, and delivering clear, defensible interpretations of what drives meaningful outcomes for the business.
• Mine large and complex product and contract datasets to surface non-obvious patterns, quantify high-impact opportunities, and generate strategic hypotheses that shape product strategy rather than simply answering pre-defined questions.
• Build and evolve data products in close partnership with Product and Engineering, translating analysis into shipped features such as embedded analytics, benchmarks, insights, and AI-powered experiences that deliver direct value to customers and internal teams.
• Wear the analytics engineering hat by owning and extending dbt models and transformations, designing scalable, well-documented, and well-tested data models that maintain consistent definitions across the warehouse and business intelligence layer.
• Architect AI-ready data structures and documentation, leveraging AI to accelerate pipeline and analysis work while ensuring data assets are organized for high confidence consumption by both humans and LLMs with minimal hallucination.
• Set the technical bar and provide mentorship through code and analysis reviews, offering technical direction to analysts, data scientists, and analytics engineers and fostering a culture of rigor, collaboration, and impactful decision-making.
• Influence senior stakeholders by bringing clarity to ambiguous, high-stakes product questions and communicating findings in a manner that drives alignment and action across Product, Engineering, and executive leadership.
• Enable self-serve analytics by designing and scaling semantic layers, clear data documentation, and analytical ecosystems that empower non-technical stakeholders to answer their own data questions with confidence and speed.
• Maintain and enhance the integrity of core product analytics systems, ensuring data reliability, performance, and scalability as usage grows and the platform evolves to support new AI capabilities.
• Collaborate with cross-functional partners to identify leading and lagging indicators of product health, translating complex data narratives into accessible stories that inform strategic choices.
• Explore evolving best practices in product analytics and AI data strategies, evaluating new tools and methodologies to continuously improve the efficiency and insightfulness of the analytical stack.
• Balance deep-dive investigative analysis with the ability to synthesize high-level trends, creating a narrative that connects granular data points to strategic product and business outcomes.
• Ensure all analytical work adheres to Ironclad's standards for quality, documentation, and reproducibility, setting an example for the rest of the organization.

Requirements

• Bring 8+ years of experience in product analytics, data science, or a closely related quantitative field, with demonstrated impact at a senior or staff level, ideally within a B2B SaaS environment where product decisions are driven by data.
• Demonstrate deep expertise in product analytics, including experimentation and A/B testing, funnel and retention analysis, causal inference, and the disciplined definition of product metrics that can withstand rigorous scrutiny.
• Show advanced proficiency in SQL, capable of writing complex queries, optimizing performance, and ensuring data accuracy across large and heterogeneous datasets.
• Show fluency in Python or R for analysis, enabling you to manipulate data, build statistical models, and create visualizations that communicate insights effectively.
• Exhibit strong ownership of data pipelines and modeling practices, with experience in tools like dbt, ensuring that data transformations are transparent, maintainable, and scalable.
• Demonstrate the ability to work with large, messy datasets, extracting signal and insight without relying on clean, pre-curated data sources.
• Communicate effectively with both technical and non-technical audiences, translating complex analytical concepts into actionable recommendations and clear narratives.
• Collaborate closely with engineering teams, understanding software development practices and contributing to technical discussions about data architecture and system design.

Practical notes

This is a hybrid role. Office attendance is required at least twice a week on Tuesdays and Thursdays for collaboration and connection. There may be additional in-office days for team or company events.