Kerem Kidron

The conversation around AI often focuses on what is possible. The more immediate question is how autonomous systems are beginning to change the way teams work. At SBC Summit Lisbon, our CPO, Dr. Eyal Loz, will join the panel ״AI Agents in Action: Autonomous Systems Transforming Modern Workflows״ to discuss how AI agents are changing product and operational workflows - from planning and reasoning to executing complex, multi-step tasks with greater autonomy. For product teams, this shift raises a new question: how do we design systems that not only serve users, but can also coordinate independent agents making decisions at speed?

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Having worked as both a Data Analyst and a Data Scientist, I’ve noticed how much these two roles can benefit from each other's habits. I’ve put together 10 practical tips to help bridge that gap. Feel free to reach out or share your thoughts in the comments

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"From days or hours to minutes - the bigger the data set, the more the insight, but without the right tool, you won't be able to go into the needed depth fast enough." > Gjorgji Miteski, VP of Analytics & Data Science at CrazyLabs (A team using ClarityQ daily for nearly two years). During a major game launch, his team hit a wall. The analyst was stuck for days trying to figure out why users were losing engagement. Miteski stepped in, attacked the problem with ClarityQ, and had a clear direction in 30 minutes - one that led to real improvements in the game experience. Gjorgji Mavrodiev, Senior Game Analyst on that same team, knows exactly what used to make deep analysis so hard: "Writing a lot of lines of complex SQL, proper aggregations, cleaning of the data, unions - this previously had to be done manually and it took a lot of hours." ClarityQ enabled the team to do exponentially more: ◆ 4 hours → 40 minutes. Deeper analysis, faster - across version releases, feature analysis, A/B tests. ◆ It's a tool for all business users. UA managers, game owners, economists - not just the data team. ◆ Mission-critical in the AI era. Fast decision-making isn't a nice-to-have. It's the game. "With ClarityQ, the insights that took days are now hours - if not minutes." Adi Ashkenazi Orly Shoavi Ronnie Sternberg CrazyLabs #aianalysis #aidatanalysis #SQL #dataanalysis #AIanalyst

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I was watching a World Cup game with some friends and we started arguing: "who do you think wins?" I had no idea, I just said "Messi?" I'm a data person, so I did what any reasonable person would do at 11pm on a workday- I started building a prediction model. What started as "I'll just look up some odds" turned into a full Dixon-Coles Poisson distributions with Bayesian team strength estimation, blended with market odds, xG form, FIFA rankings, squad depth, and head-to-head history. For WC 2026 specifically I added neutral venue correction, a draw probability boost for evenly-matched teams, and 36 qualifying matches as prior training data. My favorite part is the Algorithm tab- you can pick any two teams and see the full heatmap live. Great for settling arguments at 11pm. The whole thing runs on #Nodejs, pulls live odds via #API, and auto-refreshes every 60 seconds during matches. Open source if anyone wants to dig into the math https://lnkd.in/dAbzvaKb #WorldCup2026 #DataScience #SportsAnalytics #MachineLearning #Statistics

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In the mobile games industry, new players are harder to find. Competition has intensified and growth is non-negotiable. For Amos Adler, VP, Growth at SciPlay, that means building a system of continuous learning and a team that knows how to run it. Amos shares four lessons on user acquisition in the social casino mobile gaming category: 1️⃣ Creative is the only growth lever a UA team fully controls. 2️⃣ Measuring creative winners turns production into learning. 3️⃣ Talent potential beats years of experience, because in his experience, most UA knowledge can be learned quickly. 4️⃣ As AI changes how UA gets done, the advantage will go to those asking better questions. Swipe through the lessons on creative, measurement, talent and the future of user acquisition. ➡️ On October 29, Amos moderates “The State of Mobile Games User Acquisition” at Israel Mobile Summit 2026, Expo Tel Aviv, with UA leaders from Papaya, Z2A Digital, Fluent, Inc, PLAYSTUDIOS and Persona.ly. What questions should Amos ask the panelists? Share below 👇 #MobileGaming #IsraelMobileSummit #GrowthMarketing #SciPlay

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🚀 SQL isn’t just for analysts. It’s a superpower for Product Managers. One of the core lessons from my MBA was that a leader doesn’t need to be an expert in every discipline. But they do need to understand them well enough to ask the right questions, know what to look for, and recognize where the real constraints lie. I recently finished 4 SQL courses on DataCamp for exactly that reason. Understanding the 'how' helps me ask a much better 'why'. And when the questions are sharper, the decisions are faster. I’m not looking to replace our analysts; I’m looking to be a better partner to them. Shared language. Better outcomes. Simple as that! #ProductManagement #DataDriven #MonopolyGo

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🚀 𝗘𝗽𝗶𝘀𝗼𝗱𝗲 #𝟯 𝗼𝗳 "𝗗𝗮𝘁𝗮 𝗛𝘂𝗱𝗱𝗹𝗲" 𝗯𝘆 𝗬𝘂𝗸𝗶 𝗶𝘀 𝗟𝗜𝗩𝗘: https://lnkd.in/egrW3x_E 🚀 “𝗜𝗳 𝘆𝗼𝘂 𝘄𝗮𝗻𝘁 𝗮 𝘁𝗲𝗮𝗺 𝗼𝗳 𝗲𝘅𝗰𝗲𝗹𝗹𝗲𝗻𝗰𝗲, 𝘆𝗼𝘂 𝗰𝗮𝗻’𝘁 𝗰𝗼𝗺𝗽𝗿𝗼𝗺𝗶𝘀𝗲"- 𝘐𝘵𝘻𝘪𝘬 𝘗𝘰𝘭𝘢𝘥.  𝗜𝗻 𝗘𝗽𝗶𝘀𝗼𝗱𝗲 𝟯, we sat down with Itzik Polad, CIO at LSports, for a real talk on hiring, leadership, AI adoption, and what actually breaks when a data org starts scaling fast. 𝗜𝗻 𝘁𝗵𝗶𝘀 𝗲𝗽𝗶𝘀𝗼𝗱𝗲: 🎙️Why you can’t “kinda” compromise when building great teams 🎙️AI as a productivity multiplier, and a governance headache 🎙️How to balance speed, security, and business goals without losing your mind 🎙️Why monitoring Сloud cost isn’t enough, you need to prevent it 🎙️Hiring for potential, and building leaders from within If you’re a CTO, CIO, VP Data, or Director of Data Engineering, this is your kind of AI podcast: practical, honest, and zero fluff. 𝗪𝗵𝗼 𝘄𝗼𝘂𝗹𝗱 𝘆𝗼𝘂 𝗹𝗶𝗸𝗲 𝘁𝗼 𝘀𝗲𝗲 𝗮𝘀 𝗮 𝗴𝘂𝗲𝘀𝘁 𝗶𝗻 𝗼𝘂𝗿 𝗻𝗲𝘅𝘁 𝗲𝗽𝗶𝘀𝗼𝗱𝗲𝘀? 𝗡𝗼𝗺𝗶𝗻𝗮𝘁𝗲 𝘆𝗼𝘂𝗿 𝗳𝗿𝗶𝗲𝗻𝗱𝘀 𝗮𝗻𝗱 𝗰𝗼𝗹𝗹𝗲𝗮𝗴𝘂𝗲𝘀 𝗮𝘀 𝗠𝗼𝗱𝗲𝗿𝗻 𝗗𝗮𝘁𝗮 𝗟𝗲𝗮𝗱𝗲𝗿𝘀! 👇

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The demo gets executive attention. Continuous improvement earns user trust. I've been thinking about what really makes an AI product successful. A great demo proves what's possible. A successful POC proves the technology works. Neither guarantees long-term value. The real challenge starts after deployment. That's when the questions change: - Are people actually using it? - Is the quality improving or slowly degrading? - How quickly are we learning from user feedback? - Is the product becoming part of the daily workflow? AI products shouldn't be managed like traditional software. Shipping is just the beginning. The teams that succeed won't necessarily build the best models. They'll build the best process for continuous adoption, evaluation, and improvement. How do you measure whether your AI product is getting better after it goes live?

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Our portfolio company Sett has raised $30M! User acquisition remains one of the most critical and hardest-to-optimize areas in gaming. High costs, constant creative demand, and iterative processes make it structurally complex. Sett approaches this challenge end-to-end, building a more systematic and scalable model through AI agents. From creative exploration to production, deployment, and continuous learning, they are covering the full loop. Congrats to Amit & Yoni, and to the entire Sett team 💜

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