$ cat jobs/specialist-product-owner-actimize-nice-63f15106ed3d.json
Specialist Product Owner, Actimize
At NiCE, we don’t limit our challenges. We challenge our limits. Always. We’re ambitious. We’re game changers. And we play to win. We set the highest standards and execute beyond them. And if you’re like us, we can offer you the ultimate career opportunity that will light a fire within you. So, what’s the role all about? This role offers the opportunity to shape products that combat financial crime while ensuring adherence to complex regulatory requirements and working as a Product Owner in a matrix structure. The ideal candidate will combine domain expertise, strategic thinking, technical acumen, and a practical understanding of Generative AI and Agentic AI concepts to deliver impactful solutions. How will you make an impact? Product Strategy & Roadmap Development Define and execute the product vision, strategy, and roadmap for FCC solutions. Align product initiatives with organizational goals and client needs, focusing on innovation and compliance. Regulatory and Industry Expertise Stay updated on global financial crime regulations and ensure product offerings meet compliance requirements. Analyse emerging trends in financial crime to identify opportunities for product enhancement. Solution Design & Development Collaborate with cross-functional teams, including domain experts, engineers, data scientists, and AI specialists, to design solutions for the Financial Crime and Compliance platform, including AI-assisted and Agentic AI-enabled workflows where relevant. AI & Agentic Innovation Identify practical opportunities to apply Generative AI and Agentic AI concepts, such as AI agents, workflow automation, decision support, and assisted investigations, to improve product outcomes and user productivity. Translate AI/Agentic AI concepts into clear product requirements, user stories, acceptance criteria, and measurable business value while partnering with engineering and data science teams. Demonstrate conceptual understanding and some hands-on exposure with LLM-based s
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