Role Description Join MGID as a Data Scientist and become part of a team shaping the future of AdTech! We are a forward-thinking team striving to develop and implement cutting-edge algorithms for optimizing advertising campaigns in AdTech. Currently, our primary goal is to enable CPA (Cost Per Action) campaigns to achieve the following objectives: - Hit CPA targets by ensuring conversions occur at a specified cost. - Scale conversions volume while maintaining alignment with the CPA target. - Maximize conversions within the client’s budget. This role offers the unique opportunity to contribute directly to the development of these solutions, driving innovation in the AdTech space and shaping the future of campaign optimization. We are looking for a self-driven, proactive, and results-oriented professional who is not afraid to take ownership: analyze data, identify bottlenecks, and come up with practical solutions quickly. Qualifications - Proven experience in data science, with a strong understanding of optimization problems. - Proficient in Python and SQL, including data manipulation and feature engineering. - Expertise in machine learning techniques and tools (e.g., CatBoost, XGBoost, TensorFlow, PyTorch, scikit-learn). - Experience with big data technologies (Spark, Hadoop) and processing large datasets. - English: Intermediate/Upper-Intermediate. Requirements What You Will Do: - Data Analysis: Explore large-scale event data to identify trends, patterns, and anomalies, generate insights/ideas, evaluate campaigns’ performance, and support decision-making based on findings. - Algorithm Development: Design, implement, optimize and test algorithms for CPA optimization. - Modeling and Optimization: Build and refine predictive models to improve decisions for CPA campaigns, ensuring alignment with business objectives. - Stakeholder Collaboration: Work closely with product/business/tech teams to turn technical findings into actionable decisions. - Experimentation and Validation: Conduct and evaluate A/B tests to assess the impact of algorithm changes. - Visualization and Reporting: Create dashboards and visualizations to track key performance indicators (KPIs) and communicate findings to stakeholders. Benefits - Be part of a company where you feel supported, connected, and have the flexibility to excel personally and professionally. - Opportunity to work in an innovative, results-driven AdTech team. Company Description MGID is a global advertising platform helping brands reach unique local audiences at scale. In MGID we empower brands and publishers to work together transparently through our privacy-first targeting technology to enable advertisers to drive performance and awareness, and publishers to retain and monetize their audiences. Today, we’re creating unique technologies and with your help, we are looking to aim even higher.

• Conduct forensic reviews of financial data to detect discrepancies, cost drivers, and margin exposure • Build and maintain forecasting models for revenue, expenses, and profitability • Deliver forward-looking insights to support strategic and operational decisions • Develop and enhance cost allocation approaches across regions and programs • Partner with Finance, PMO, and Operations teams to ensure data accuracy and consistency • Create dashboards and visual reporting tools (e.g., Power BI) for leadership stakeholders • Integrate and analyze data from multiple systems to provide a comprehensive financial view • Collaborate cross-functionally to drive financial strategy and performance improvements

• Liderar tecnicamente projetos de modelagem e análise, desde a concepção até a entrega em produção, garantindo qualidade técnica e alinhamento com os objetivos de negócio; • Definir e disseminar boas práticas de engenharia de variáveis, arquitetura de features e estruturação de dados, elevando o padrão técnico do time; • Analisar dados para gerar insights estratégicos em diferentes frentes do negócio, identificando oportunidades de crescimento e riscos emergentes e traduzindo essas descobertas em decisões concretas; • Desenvolver, implementar e validar modelos e ferramentas de machine learning — como modelos de scoring, propensão, churn e segmentação — sendo referência técnica para o time nas escolhas de abordagem e metodologia.

• Explore large-scale event data to identify trends, patterns, and anomalies • Analyze data to support decision-making • Design, implement, optimize and test algorithms for CPA optimization • Build and refine predictive models for CPA campaigns • Collaborate with product/business/tech teams to turn findings into actionable decisions • Conduct and evaluate A/B tests • Create dashboards and visualizations to track KPIs