Senior Product Manager, AI / Data Classification

Zscaler•San Jose, CA

5d•Hybrid

About The Position

Zscaler accelerates digital transformation to ensure our customers can be more agile, efficient, resilient, and secure. As an AI-forward enterprise, we are constantly pushing the envelope, leveraging the world’s largest security data lake to power our cloud-native Zero Trust Exchange platform. This innovation protects our customers from cyberattacks and data loss by securely connecting users, devices, and applications in any location. Here, impact in your role matters more than title and trust is built on results. We say, impact over activity. We seek innovators who actively use AI to amplify their impact and who thrive in an environment where we leverage intelligent systems to stay ahead of evolving threats. We believe in transparency and value constructive, honest debate —we’re focused on getting to the best ideas, faster. We build high-performing teams that can make an impact quickly and with high quality. To do this, we are building a culture of execution centered on customer obsession, collaboration, ownership, and accountability. We value high-impact, high-accountability with a sense of urgency where you’re enabled to do your best work and embrace your potential. If you’re driven by purpose, thrive on solving complex challenges, and want to be part of the team that’s helping to secure the AI age, we invite you to bring your talents to Zscaler and help shape the future of cybersecurity.

Requirements

Product Management experience, with meaningful ownership of data security/privacy products. Product management experience can be substituted with data science experience
Demonstrated experience shipping products that use AI/ML and/or LLMs in production, with a strong understanding of data classification approaches across structured and unstructured data
Expertise in evaluation and quality measurement for classification systems and building/maintaining golden datasets
Ability to design human-in-the-loop workflows for high-stakes labels, covering review/approval, exception handling, and audit trails
Experience driving cost/performance tradeoffs for AI at scale, including token/compute budgeting, model selection, and managing latency for large corporations

Nice To Haves

Strong technical collaboration skills with ML engineering and data science
Familiarity with embedding-based similarity and semantic retrieval for classification and deduping (vector DBs, ANN indexing, chunking strategies)
Prior work in DSPM/DLP/CASB/insider risk where false negatives have high blast radius and you have designed processes to manage that risk

Responsibilities

Partner closely with a focused engineering, data science/ML, design, sales, and customer success to deliver accurate, scalable classification, clear policy outcomes, and measurable customer value
Own the classification taxonomy, labeling standards, and policy model (custom categories, confidence thresholds, inheritance, overrides, exceptions)
Drive accuracy improvements by managing precision/recall targets, sampling strategies, tuning workflows, and customer feedback loops
Set requirements for scanning at enterprise scale (billions of objects, large tables), optimizing cost, latency, and coverage
Define how the platform combines pattern-based detectors, NLP/ML, and LLM-assisted classification for structured and unstructured data