Staff AI & Automation Data Analyst

Pure Storage

Bangalore, India

Full Time

Posted August 24, 2026

Power Generation

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Job Description

And because we understand the value of bringing your full and best self to work, we offer a variety of perks to manage a healthy balance, including flexible time off, wellness resources, and company-sponsored team events.

Key Responsibilities

Everpure is moving from an application-centric world to a data-centric one — where AI depends on trusted, well-governed, and contextualized data across the business. As part of this shift, we're looking for a Staff Analyst, AI & Automation to lead the technical build of AI and applied ML capability inside the analytics function.
This is an individual contributor role — you will not manage people. Your leverage comes from what you build and ship: AI agents, applied ML models, and automated analytics workflows that change how insight gets produced, working closely with cross-functional teams to bring these to life.
AI is changing by the week, not by the year. We're not looking for someone who adopts AI once it's already mainstream — we want someone who is naturally ahead of the curve: scanning what's new, testing it quickly, and bringing it into the team before it's obvious to everyone else. This role has real freedom to experiment, rethink existing approaches, and change course as the field moves.
Own the technical build of AI-powered analytics tools — including RAG pipelines, LLM-assisted querying, agent workflows, and applied ML models. Our current stack includes Glean, Gemini, Claude Code and Snowflake Cortex, and we expect that to change; we care more that you pick up new tools quickly than that you already know ours.
Apply data science methods (forecasting, segmentation, anomaly detection, classification) where a problem genuinely calls for them — though the core of this role is LLM- and agent-based automation rather than classical modelling.
Design, build, and deploy AI agents for analytics use cases — e.g. agents that answer business questions from data, auto-generate insights, flag anomalies, or draft recurring reports without manual pull.
Identify and prioritize high-value opportunities to automate manual, repetitive analytics work with AI, and independently prototype, test, and productionize those solutions.
Propose and maintain a rolling technical roadmap for AI and data science work in the analytics function — a living document you revisit and adjust as the field moves, not a fixed annual plan.
Write the SQL/Python needed to support your own models and analysis, collaborating with the broader technical organization on any shared systems your work touches.
Champion data governance and AI-readiness practices — data quality, semantic context, lineage — so AI agents and models are working from trustworthy, well-understood data.
Partner with business stakeholders to scope problems, and with IT/engineering/data platform teams to ensure AI and automation initiatives are supportable at scale.
Pair with and review the work of other analysts applying data science and AI methods — sharing technical judgment through collaboration, not people management.

Requirements

7+ years of experience across data science, applied AI/ML, or advanced analytics, with a track record of Staff-level individual-contributor technical ownership.
Bachelor's degree in statistics, computer science, math, or a related field (or equivalent practical experience).
Strong Python skills for data science and automation — comfortable with libraries like pandas, scikit-learn, and building/calling ML models, not just scripting.
Hands-on experience building with LLMs and AI agents — e.g. RAG pipelines, agent orchestration frameworks, prompt engineering, or tool-calling — and shipping something people actually use, not just experimenting.
Strong SQL skills to support your own models and analysis, and enough data modeling fluency to work well with broader technical teams.
In your application, tell us about something you built with LLMs in the last 6 months that people actually use — what it does, who uses it, and how often.
High adaptability: comfortable dropping an approach that's no longer the best one, learning a new tool quickly, and adjusting plans as the AI landscape shifts, sometimes within the same quarter.
Excellent communication skills — able to explain data science/AI concepts and trade-offs in a way that builds confidence with non-technical stakeholders and leadership.
Strong ownership mindset: comfortable with ambiguity, prioritizing across competing asks, and driving projects independently.
Experience with agent orchestration frameworks (e.g. LangChain, LlamaIndex, or similar) or building multi-step autonomous agents.
Experience with MLOps practices — model monitoring, versioning, or deployment pipelines.
Exposure to data governance, semantic data modeling, or data classification concepts (e.g. knowledge graphs, metadata tagging).
Familiarity with cloud data warehouses (Snowflake, BigQuery, Redshift) and orchestration tools.
Practical experience applying statistical or ML techniques (forecasting, anomaly detection, classification, clustering) to business problems.
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