AI & Data · Santiago, Santiago Metropolitan Region, Chile

A data science role like this typically asks for solid statistics and machine learning fundamentals, comfort with Python and SQL, and the ability to turn messy data into models that product and engineering teams can use. A strong candidate brings clear communication, sound judgment about which models matter, and care in validating results before they reach a live system. Since the company builds infrastructure for real time ad auctions and cookieless retail media, interest in fast, reliable modeling under real constraints helps.

How it fits the AI era

AI tools now speed up routine modeling work such as feature exploration, code drafting and report writing, so the role shifts toward framing the right questions and checking that outputs hold up. What stays human is judgment about data quality, tradeoffs in model design, and explaining findings to product and engineering partners. In auction and retail media settings, careful evaluation and monitoring matter more than ever because small errors carry real cost.





How it builds AI experience

This role offers hands on practice applying machine learning to live advertising and retail media problems, including model evaluation and monitoring. Building that experience can start with personal projects on auction or bidding data, open datasets, and careful study of how models behave in production. Working closely with engineering on deployment and feedback loops is where the deepest learning happens.

Worth showing

- Python and SQL
- Machine learning fundamentals
- Model validation and monitoring

This is our own read, written from the employer's posting and the company's Spotlight. The employer's posting is the source of truth for the role.

About Topsort

AI and machine-learning infrastructure for cookieless retail media networks and real-time advertising auctions, per the company.

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📌 Data Science (Santiago)
🏢 present of AI
📍 Santiago