AI is everywhere — headlines promise instant insights, automated decisions, and revolutionary change. But can AI truly replace the expertise, rigor, and strategy of solid Data Science?
Join us for an exclusive Meetup for data scientists, analysts, and AI enthusiasts to explore this question in depth. Discover how Generative AI, Machine Learning, and proven Data Science practices can complement each other to create measurable, real-world impact — beyond the hype. Hear firsthand from industry experts about successes, challenges, and lessons learned from practical applications.
RegisterOctober 7, at 5 p.m.
PAYBACK office, 28 Towarowa Street, Warsaw
Gain insights from leading professionals at PAYBACK and Goldenore, including a keynote by Norbert Wirth, Global VP Data at PAYBACK GROUP. Learn how to harness AI responsibly and understand the opportunities and limitations of today’s technology. This is your chance to exchange ideas, network with peers, and connect with professionals navigating similar challenges in AI and Data Science.Reserve Your Spot Now – Spaces Are Limited!
Does AI Eat Data Science for Breakfast?
Beyond AI Hype: CausalML and Optimization in Push Notifications
This talk presents a real-world case study on optimizing push notifications. Initially, an optimizer was used to assign users to campaigns, but every user still faced the same fixed notification frequency. This one-size-fits-all rule proved limiting — some users were overwhelmed and opted out, while others received too few notifications and stayed disengaged.
To address this, we enhanced the approach with CausalML and advanced optimization techniques. By estimating individual treatment effects and reusing the solver to allocate notifications under business constraints, we established personalized frequency limits for each user. The outcome was higher engagement without a significant rise in opt-outs.
The key takeaway: AI hype does not replace Data Science fundamentals. Causal inference and optimization remain essential in building effective, real-world systems.
AI: Breakfast of Champions or Fast Food?
Will GenAI meet the majority of business needs in the future? People often talk about possibilities, but less frequently about limitations - yet understanding them gives us greater control and unlocks new, real-world applications. In an era where LLMs are increasingly becoming the heart of modern IT systems (Software 3.0), the question arises: are we building solutions with care, following ML/DS processes and best practices, or are we just serving quick, uncontrolled implementations? Who really eats whom for breakfast? And what can we gain when GenAI (LLMs) and ML & solid Data Science practices sit at the same table, complementing each other and creating value that can be controlled and scaled?
Hybrid Intelligence: Supercharging GenAI Agent Efficiency with ML Precision
Generative AI multi-agent systems promise unprecedented personalization but often struggle with high operational costs and inconsistent output quality, especially when directly processing complex data like customer profiles and coupon descriptions. This talk unveils a pragmatic, hybrid approach that significantly improves efficiency and accuracy. We demonstrate how integrating specialized Machine Learning (ML) agents to pre-filter and score relevance (e.g., for coupons or Next Best Actions) significantly reduces expensive LLM processing. By feeding GenAI agents only the top 3-10 highly relevant, ML-validated options, we achieve superior personalization and optimize GenAI cost.
Norbert Wirth
Global VP Data at PAYBACK, draws on his experience in end-to-end Data Science product development, Machine Learning and Artificial Intelligence, the digital ecosystem, data operating models, and algorithm development. His professional experience, continuous engagement in various industry bodies, and regular appearance at international conferences as keynote speaker and moderator make him a thought leader in data driven solution development, applied artificial intelligence and data management. Before joining PAYBACK, Norbert was ramping up and managing Sqooba Germany and SUPERCRUNCH by GfK, two Data Science startups. He worked as Director Data Science and AI at PwC, Global Head of Data and Science and Global Head of Innovation and Digital at GfK.
Arkadiusz Słowik
Data Scientists at PAYBACK, is a passionate Data Scientist dedicated to solving complex business challenges with data-driven insights. A former hackathon enthusiast, he brings a creative and competitive edge to problem-solving. In his daily work, he focuses on end-to-end machine learning workflows, from data to deployment. Beyond projects, he is deeply committed to sharing knowledge and fostering collaboration within the data and AI community.
Vladimir Aleksiejczenko
CEO of DataWorkshop, with over 10 years of hands-on experience in the AI industry - Machine Learning and Data Science. He advises both small and large companies on how to build stable and high-quality solutions based on AI models.
He has personally trained and deployed hundreds of ML models into production. Today, in the context of LLMs, he focuses mainly on what works reliably in production.
He is also a podcaster (Biznes Myśli), the author of 7 original specialist courses in AI, as well as other educational initiatives, which have altogether engaged over 10,000 participants.
Rafał Latkowski
Principal AI & Data Scientist, expert in Machine Learning, Data Science, and Data Analytics with over 20 years of experience in the IT industry. Throughout his career, he has supported leading companies in finance, telecommunications, loyalty programs, and retail.
He specializes in managing the development and implementation of advanced analytical tools. For over 15 years at PAYBACK, he has been responsible for driving and aligning Data & Consumer Analytics solutions, fostering collaboration among analytical teams, and shaping the future of the data landscape through the adoption of new technologies.
He holds a degree in Computer Science and Mathematics from the University of Warsaw and a doctorate from the Polish Academy of Sciences. Outside of his professional endeavors, he continues his academic pursuits in Machine Learning, engaging in research and teaching activities.
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