
1 - 10 funcionários
Fundada em 2024
💼 Consultoria
📣 Marketing
📦 Logística
Consulting • Marketing • Logistics
A Lago é um serviço de recrutamento que conecta agências de marketing digital e empresas a talentos globais de alto nível provenientes de mercados emergentes. Desde 2015, a Lago é especializada em fornecer soluções de contratação remota, oferecendo vagas como assistentes virtuais, gerentes de social media e representantes de suporte ao cliente. A empresa busca potencializar tanto as agências quanto os profissionais talentosos, facilitando oportunidades de trabalho recompensadoras e permitindo que os negócios cresçam de forma eficiente e com ótimo custo-benefício.
🕒 Março 10
🇵🇭 Filipinas – Remoto
💵 $20 - $40 / hora
⏰ Tempo Integral
🟠 Sênior
🤖 Engenheiro de Machine Learning
👻 Score fantasma 40%
🗣️🇺🇸🇬🇧 Inglês obrigatório
De onde vem esta vaga
Publicada na página de carreiras da Lago, não em um site de vagas
Vista pela primeira vez pelo Remote Rocketship Março 10
Ainda aberta no site da Lago. Verificada 2 dias atrás.

1 - 10 funcionários
Fundada em 2024
💼 Consultoria
📣 Marketing
📦 Logística
Consulting • Marketing • Logistics
A Lago é um serviço de recrutamento que conecta agências de marketing digital e empresas a talentos globais de alto nível provenientes de mercados emergentes. Desde 2015, a Lago é especializada em fornecer soluções de contratação remota, oferecendo vagas como assistentes virtuais, gerentes de social media e representantes de suporte ao cliente. A empresa busca potencializar tanto as agências quanto os profissionais talentosos, facilitando oportunidades de trabalho recompensadoras e permitindo que os negócios cresçam de forma eficiente e com ótimo custo-benefício.
• Build and improve inventory demand forecasting models using ML and statistical methods. • Own ML models end-to-end: data collection → feature engineering → training → deployment → monitoring → iteration. • Develop decision systems that support inventory planning, pricing, and demand decisions. • Build and maintain data pipelines and API integrations for external and internal data sources. • Work with messy real-world data to ensure model reliability through rigorous validation and testing. • Implement LLM/AI-agent workflows to translate domain logic into automated processes. • Operate independently in a small team, setting priorities, unblocking challenges, and communicating tradeoffs clearly.
• 5+ years of Python experience in production ML systems (beyond notebooks/research). • Deep experience with statistical modeling, including ensemble methods, kNN, calibration, cross-validation, and feature engineering. • Expertise in time-series modeling & forecasting, including seasonality, trend decomposition, safety stock, and demand planning. • Proven track record of shipping ML models that drive real business decisions (forecasting, pricing, demand planning). • Strong intuition for messy, real-world data, including bias correction, stale signal handling, error cancellation, and distribution shifts. • Experience with API integration and data pipeline architecture at scale. • Hands-on experience with LLM/AI-agent workflows, including prompt engineering and evaluation frameworks. • Proven ability to validate models rigorously: LOO, backtesting, production vs offline metric gaps. • Self-directed, comfortable in a fast-evolving, small team environment. • Nice-to-Have Qualifications: Experience in Amazon marketplace, e-commerce, or retail analytics. • Familiarity with similarity-based methods (kNN, embeddings, vector search). • Experience maintaining long-lived model systems (v1 → v30+ iteration cycles). • Prior startup or founder-adjacent experience.
• Remote Work: Work from anywhere—our team is global, and we value work-life balance. • Growth Opportunities: As a key player i you’ll have the chance to shape your role and grow with us. • Innovative Culture: Join a team that is passionate about leveraging data to solve challenges and drive success in a rapidly evolving market.
Candidatar-seMelhore suas chances de conseguir uma entrevista verificando sua pontuação de currículo antes de se candidatar.