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Advertising used to be a simple affair. You bought a newspaper ad, a billboard, or a 30-second slot on TV. Everyone saw the same thing, and the attribution was a shrug and a smile. Today, advertising is driven by data, focused, and programmatically optimized. Yet, inside all this precision, new chaos is born.
For a retailer, the website and apps become advertising space to be sold, and retail media has surged as a new frontier. This market stands at $150 billion for 2024 and is forecasted to swell beyond $280 billion by 2027 with the seismic shift in brand-to-consumer communication.
Yet, studies show that marketers lose a significant part of their digital ad spend through various attribution failures, fragmented data systems, and inflexible measurement models. It’s not just waste; it is a broken feedback loop. Businesses are paying more and understanding less.
This is the crux of the problem that Mayukh Maitra and his team at one of the world’s largest retail media networks set out to address. Mayukh, a Senior Data Scientist, helped design a genetic algorithm-based model for optimizing media mix, one that adapts in real time to real-world constraints.
Why genetic algorithms? Because traditional models, though statistically robust and widely adopted, fail to account for the operational complexity of retail media. The traditional Bayesian framework doesn’t easily accommodate constraints like hard budgets, SKU-level granularity, and regional campaign overlaps. On the other hand, genetic algorithms mimic biological evolution, testing and adapting thousands of scenarios to identify the optimal ad mix under shifting business goals.
Across multiple real-world scenarios, this approach was put to the test several times, especially when considering volatile demand patterns or cases where campaigns interacted in rather complicated ways. The model ended up dominating in all of them; hence, in terms of stability, adaptability, and predictive potential, it has proved its practical worth where conventional models fail.
But building a model is only half the battle. Translating that model into something actionable is where most solutions falter. Maitra led the design of a real-time visualization platform that connects scientific output with campaign decision-making. Teams can now interact with model recommendations, experiment with different media spend strategies, and adjust campaigns dynamically, all in a single interface.
This tool has led to sharper budget allocation, reduced turnaround time for model refresh cycles, and better alignment between media investments and business outcomes.
Mayukh’s role, however, extended far beyond data pipelines. He contributed laboriously to team culture by mentoring junior data scientists, conducting numerous interviews, and serving for the betterment of the team culture. He helped build not just models, but also teams.
Technically, he brings fluency in data science. But his edge lies in his philosophy: ask better questions before writing better code. He approaches problems not as abstract math puzzles, but as systems that must integrate with people, platforms, and imperfect data.
Recognition has followed, but not in the form of public promotion. Maitra was awarded the Indian Achiever’s Award (2023–24), the Data Science Dynamo honor at the 2024 NRI Achievers Awards in London, and the AIBCF Professional of the Year award. These are not medals given for heroism; they are reflections of how impactful, behind-the-scenes work has the power to change an entire category.
So, where does the field go from here? The team is now investigating hybrid models that integrate Bayesian reasoning with genetic adaptability, merging statistical inference with computational optimization..
Third-party cookies are disappearing, and regulation is getting tighter. This situation will create renewed interest in first-party data and explainable models. Mayukh`s framework puts him at the top, providing perfect interpretability combined with performance. The future is not about piling in more variables; it is about surfacing the right variables at the right time and in the right way.
Still, it never guarantees smooth sailing. Retail media will remain messy, fragmented, and fiercely competitive. However, building systems that respect the complexity of the space and that adapt rather than dictate. Teams like Mayukh’s are guiding the industry toward smarter, leaner, more accountable advertising.
In the end, Mayukh Maitra’s story isn’t about breakthrough genius or lone-wolf innovation. It is about stewardship. About showing up with curiosity, resisting the temptation to over-engineer, and creating solutions that evolve with the landscape. In a sector driven by noise, that kind of quiet clarity may be the most valuable currency of all. Not as a solo effort, but as part of a growing data science movement focused on turning complexity into clarity, at scale.
