data science manager - ai solutions

Required data science manager - ai solutions
company description
A palo alto networks company, is the global leader in identity security, trusted by organizations around the world to secure human and machine identities in the modern enterprise. Our ai-powered identity security platform applies intelligent privilege controls to every identity with continuous threat prevention, detection and response across the identity lifecycle. with identity security, organizations can reduce operational and security risks by enabling zero trust and least privilege with complete visibility, empowering all users and identities, including workforce, it, developers and machines, to securely access any resource, located anywhere, from everywhere.
job description:
as a data science manager at palo alto, you will lead a high-performing team developing ai/ml solutions that secure the digital worlds most sensitive assets. youll play a key role in driving innovation, productizing Machine Learning systems, and applying advanced modeling techniques to real-world cybersecurity challenges.
in this leadership role, youll balance hands-on technical involvement with strategic oversight, guiding projects from research to production. youll work closely with data scientists, engineers, analysts, product managers, and cyber-security researchers to build intelligent features across our identity security portfolio
lead, mentor, and grow a team of applied data scientists working on Machine Learning models that power ai-driven cybersecurity solutions.
own the end-to-end development lifecycle of ai projects - from exploratory research and experimentation through scalable deployment and optimization.
partner with engineering and product leadership to define technical strategies, prioritize initiatives, and ensure successful model delivery.
drive innovation across a broad spectrum of ml domains including supervised/unsupervised learning, anomaly detection and generative ai.
provide hands-on guidance in model development using tools such as scikit-learn, pytorch, tensorflow, and hugging face. - ensure high-quality execution through strong code review practices, reproducibility, and model evaluation frameworks.
champion best practices in mlops, data governance, explainability, and monitoring.
keep pace with academic and industry advances and translate cutting-edge research into productized capabilities.