Product Manager – AI & Data Science needed at Meltwater

What We’re Looking For:

• We’re looking for a Product Manager to help drive Meltwater’s next-generation AI enrichment capabilities — including categorization, sentiment, topics, narratives, and emerging LLM-based features.
• You’ll work closely with the Product Lead for Enrichments & Automation to translate data science developments into real product improvements and customer value. Your focus will be on understanding model behavior, defining quality measures, and providing insights that guide prioritization, product improvements, and quality decisions across our enrichment portfolio.
• This role is well-suited for someone who enjoys both the technical side of applied AI and the product thinking required to turn models into meaningful user outcomes.

What You’ll Do:

• Help define and prioritize enrichment improvements based on product strategy and customer impact.
• Translate model capabilities into product value by understanding where enrichment quality matters most in customer workflows.
• Collaborate with Data Scientists to turn experiments and research findings into clear product iteration plans.
• Monitor quality and usage: Design and maintain dashboards and evaluation frameworks that track performance, adoption, and impact.
• Support rollouts and internal understanding through clear documentation, release notes, and internal enablement for teams that rely on enrichment outputs.

What You’ll Bring:

• 4+ years of experience in Product, Data, or Applied ML/NLP roles working with structured data or enrichment models.
• Strong customer-centric mindset: You care how models perform in real workflows, not just in benchmark tests.
• Ability to turn complexity into clarity: You can turn abstract or technical concepts into clear product decisions and requirements.
• Collaboration & coordination strengths: You work effectively across Data Science, Engineering, and customer-facing Product Managers – aligning priorities and enabling others to build value on top of the platform.
• Awareness of the evolving AI landscape:  You stay informed about LLMs and enrichment techniques and can distinguish practical value from hype.
• Analytical and evaluation fluency: You are comfortable evaluating model performance and making trade-offs on quality, coverage, and scalability.
• Clear communication and narrative framing: You can explain why improvements matter, not just how they work, to stakeholders across product, GTM, and engineering.

Nice to Have

• Experience with NLP tasks like sentiment, entity extraction, or classification.
• Exposure to LLM evaluation or prompt design.
• Experience working with multilingual datasets.
• Familiarity with media intelligence, social listening, or analytics platforms.