Data Science Manager

Job not on LinkedIn

🔥 2 minutes ago

🗣️🇧🇷🇵🇹 Portuguese Required

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Logo of Skeelo

Skeelo

51 - 200 employees

Founded 2019

👥 B2C

📱 Media

B2C • Media

Skeelo is a subscription-based digital reading app that gives users access to monthly ebooks and audiobooks through partnerships with publishers. Users download the app from the App Store or Google Play, receive new titles each month as part of their plan (with options to swap the book of the month), and keep the books they receive permanently. Skeelo focuses on on-the-go reading and listening, offering a curated digital library for consumer readers and audiobook listeners.

📋 Description

• Structure the Machine Learning and AI area within the Data team, defining the stack, architecture, model governance, and MLOps best practices from the first model through to operating at scale • Develop and put into production predictive models using our proprietary data, focusing on content recommendation, propensity and personalization of the reader's journey (churn, reactivation, next best book, ideal format) • Bring GenAI and AI agents into daily operations and product: curation assistants, automation of internal workflows, and conversational experiences that increase engagement and productivity • Recruit, train, and develop the team of data scientists, playing a leading role in building the team that will grow with you • Work closely with Data Engineering, Analytics, Product, Growth, and Software Engineering to integrate models into products, the CRM, and business decisions, closing the full cycle from training to monitoring, retraining, and measuring impact on metrics such as D28, MAU and minutes consumed

🎯 Requirements

• Proven experience setting up Machine Learning or Data Science functions, from scratch or in early-stage maturity • Strong hands-on technical skills in ML, with proficiency in Python and the modern data ecosystem (e.g., Databricks, AWS, SQL) • Real ML use cases in production and at scale with measurable business impact, not just proofs of concept • Mastery of the full model lifecycle: training, deployment, performance and drift monitoring, retraining, versioning, and documentation (MLOps in practice) • Experimental mindset: A/B testing culture, causal measurement, and evidence-based decision making • Advanced or fluent English

🏖️ Benefits

• Remote work model • On-site meetups in São Paulo, SP

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