This data science mistake is killing 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 learning products, fighting fraud and spam, or trying to connect AI work to real business outcomes, this episode delivers practical lessons from one of the most demanding product environments in tech. 🎧 Subscribe to Builders for more conversations with leaders shaping the future of AI, data science, engineering, and product innovation.#DataScience #AI #MachineLearning #MLOps #FraudDetection #SpamDetection #Truecaller #DataEngineering #ProductLeadership #BuildersPodcastChapters(00:00) How Truecaller Builds Data Science Teams That Ship(01:21) Liniker Seixas’ Journey Into Data Science Leadership(03:35) Why Companies Get Data Science Teams Wrong(04:04) The Magic Wand Fallacy in Data Science Hiring(05:54) Why Data Scientists Shouldn’t Own Everything Alone(07:36) Why Data Science Needs Engineering Support to Scale(08:08) What a Well-Balanced Data Science Team Looks Like(10:09) How Truecaller Keeps AI Models Fresh Against Spam and Fraud(10:51) Why Fast Delivery Beats Eight-Month AI Projects(12:38) What Separates Successful Data Products From Failed Ones(13:22) Why Business Impact Matters More Than Perfect Models(14:28) How to Keep Data Science Anchored to Product Outcomes(16:11) How Truecaller Measures Success Through User Feedback(17:18) Why Guardrail Metrics Matter in Data Science Experiments(18:28) How Truecaller Reframed Spam Detection Around User Behavior(20:38) Building ML Models in a Cat-and-Mouse Fraud Environment(22:16) Why Model Drift and Continuous Learning Matter(24:07) How to Hire Data Scientists for Curiosity and Learning Speed(26:35) Internal Mobility and Growth Inside Data Science Teams(28:28) Why Adaptability Beats the Perfect CV in AI Hiring(30:00) How AI Is Changing Technical Skill Assessment(30:25) Why Data Scientists Must Stay Relevant(31:46) The Role of Senior Data Scientists in Scaling Teams(33:19) Building a Five-Year Vision for Data Science Teams(35:45) How to Prioritize Ideas Across a Long-Term Roadmap