THE ROLE

Pure Storage is seeking a Product Data Scientist Analyst to support the Systems Product Management team within the Core Platform Business Unit (CPBU). This team owns our core systems portfolio - that supports the majority of Pure's revenue at scale.

This is an AI‑first analytics role focused on answering fundamental questions about product performance, product forecasting, transition management, and adoption across systems SKUs and features. You will combine telemetry, bookings data, pipeline, and qualitative feedback to help systems PM team understand where the portfolio is winning, where it is under‑performing, and why.

You will partner closely with Systems PMs, Engineering, Finance, and GTM teams to:

• Build and refine product‑level forecasts.
• Root‑cause low or uneven adoption of products and features.
• Deliver recurring and ad‑hoc insights that shape roadmap, pricing, and packaging decisions.

If you are excited about using data and AI to understand complex systems products, forecast demand, and explain what’s really happening behind the numbers, this role is for you.

WHAT YOU'LL DO

Core Product & Portfolio Analytics

• Build and maintain dashboards, recurring reports, and simple self‑serve views that help Systems PMs and leaders understand product health across customer segments, geos, and routes to market.
• Own core analytics for a set of systems products and features (e.g., platforms, SKUs, configurations), including definition and tracking of key KPIs such as adoption, utilization, attach, renewal, and revenue contribution
• Translate open‑ended product questions (e.g., “Why is adoption lagging for this product?”) into structured analytical approaches and clear, defensible findings.

Forecasting & Capacity Insights

• Partner with Systems PMs and Finance to build and refine product‑level forecasts for units, capacity, and revenue across SKUs, configurations, and key segments.
• Use historical trends, pipeline signals, win/loss data, and telemetry to inform short‑ and medium‑term forecasts, including scenario modeling for different roadmap and pricing outcomes.
• Provide forecast variance analysis: explain where and why actuals diverged from plan (e.g., mix shift, competitive pressure, supply issues, enablement gaps), and what should change as a result.

Adoption Deep Dives & Root‑Cause Analysis

• Conduct structured deep dives on low or uneven adoption across products and features: identify patterns by customer segment, workload, configuration, region, and route to market.
• Combine telemetry, field feedback, deal data, and support signals to determine whether adoption issues are driven by discoverability, usability, performance, pricing, packaging, competitive dynamics, or operational friction.
• Produce concise “adoption narratives” that clearly state the problem, evidence, and recommended actions (e.g., roadmap changes, packaging tweaks, enablement, migration paths).
• Track the impact of corrective actions over time and close the loop with PMs and GTM stakeholders.

AI‑Driven Insights & Automation

• Use modern AI tools (LLMs, agents, RAG, copilots) to explore large, complex datasets (telemetry, logs, case data, notes) and accelerate analysis that would be difficult or too time‑consuming to do manually.
• Design and prototype AI‑powered workflows and helpers - for example, natural‑language querying over product metrics, anomaly detection and alerting, or summarization of field and customer feedback related to systems products.
• Identify recurring analytical workflows (forecast updates, adoption health checks, portfolio reviews) and automate them where possible, so you can focus on higher‑order questions and strategic insights.

Experimentation & Decision Support

• Support design and analysis of experiments and controlled changes (e.g., packaging changes, pricing trials, feature defaults) that affect systems product behavior and attach.
• Quantify the business impact of roadmap decisions and GTM changes, including revenue, margin, and utilization effects.
• Partner with Systems PMs on opportunity sizing and scenario modeling for new SKUs, and platform investments.

Data Foundations & Cross‑Functional Collaboration

• Work with Data Engineering, BI, and telemetry teams to define reliable event taxonomies, data contracts, and quality checks for key systems product signals.
• Help maintain consistent metrics definitions and semantic layers so that CPBU stakeholders are working from a single source of truth.
• Act as the primary analytics partner for a subset of Systems PMs, and as a key contributor to quarterly and annual business reviews for the systems portfolio.

WHAT YOU BRING

• 3–6+ years of experience in product analytics, data analytics, or business analytics, ideally supporting product management in B2B infrastructure, cloud, or systems‑oriented businesses.
• Demonstrated track record of building forecasts, performance reviews, and deep‑dive analyses that influenced roadmap, pricing, or portfolio decisions.
• Experience working directly with Product Management and cross‑functional partners (Engineering, Finance, Sales, Operations) on data‑driven decision‑making.
• Strong SQL skills; comfortable working with large, complex datasets and optimizing queries for performance
• Experience with at least one modern BI/visualization tool (e.g., Tableau, Power BI, Looker, or similar).
• Practical experience with Python (or a similar language) for data analysis, modeling, and building analytical workflows.
• Working familiarity with forecasting and time‑series analysis concepts; able to build, interpret, and refine forecasts in partnership with PM and Finance.
• Hands‑on experience using AI/LLM tools in analytical work (e.g., notebook copilots, NL‑to‑SQL, log and feedback summarization, internal agents/automations).
• Strong business and product sense; able to connect metrics and trends to real‑world behavior and systems product strategy.
• Excellent communication skills and the ability to produce clear, concise written narratives and presentations for executives and cross‑functional teams.
• Bias toward action, curiosity, and structured problem solving – you enjoy asking “why?” until the root cause is clear, and then helping teams decide “so what?” and “what next?”.
• Experience with systems or infrastructure products (storage, compute, VMware ecosystems, cloud platforms, or similar).
• Familiarity with product analytics platforms and event‑driven data models.
• Exposure to portfolio management or SKU‑level analytics (mix, attach, cannibalization, lifecycle).
• Experience building or integrating AI‑powered decision‑support tools for product and portfolio teams.

#LI-ONSITE