This data science mistake is killing AI projects
From Builders by Proxify by Proxify
May 27, 2026 · 44 min
About this episode
Liniker Seixas discusses how data science teams can effectively build models that deliver real business impact in AI projects.
Data science, AI, spam detection, fraud prevention, MLOps, and machine learning teams are reshaping how product companies build trust at scale. In this episode of Builders, Liniker Seixas, Senior Staff Data Scientist and Team Lead at @truecaller , explains how data science teams can move beyond experiments and build models that actually work in production.Why do so many companies fail to turn data science into business impact, and what does Truecaller do differently?Liniker shares:- How to build practical data science teams that ship real products- Why hiring “unicorn data scientists” is usually the wrong move- How data engineers, MLOps engineers, and product owners support model success- Why vanity metrics like F1 scores and accuracy are not enough- How Truecaller adapts models in a fast-moving spam and fraud environment- Why user feedback is essential for improving spam and fraud detection- How to hire data scientists for curiosity, adaptability, and learning speed- What senior data science hires bring to early-stage and scaling teams- How to build long-term technical strategy without betting everything on today’s AI trendsIf you’re building data science teams, scaling machine…
People in this episode
Guest: Liniker Seixas
Topics covered
- data science
- AI
- machine learning
- MLOps
- spam detection
- fraud prevention
- business impact
Keywords
- data science teams
- machine learning products
- model success
- vanity metrics
- user feedback
- hiring data scientists
- technical strategy
Mentioned in this episode
Organizations: Truecaller
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