Intermediate Data Science| Anti-Fraud

AB InBev is the leading global brewer and one of the world’s top 5 consumer product companies. With over 500 beer brands, we’re number one or two in many of the world’s top beer markets, including North America, Latin America, Europe, Asia, and Africa.

Created in 2022, the Growth Group unifies our business-to-business (B2B), direct-to-consumer (DTC), Sales & Distribution, and Marketing teams. By bringing together global tech and commercial functions, the Growth Group allows us to fully leverage data and drive digital transformation and organic growth for AB InBev around the world.

In addition to supporting well known global beer brands like Corona, Budweiser and Michelob Ultra, the Growth Group is home to a robust suite of digital products including our B2B digital commerce platform BEES, on-demand delivery services Ze Delivery and TaDa Delivery, and table top beer keg PerfectDraft.

We are an exceptional team, focused on understanding and supporting consumer and customer needs, harnessing new technology, and scaling growth opportunities.

We are looking for a Data Scientist to join the TaDa LATAM Anti-Fraud team, developing models, algorithms, and analytical strategies to identify fraudulent behavior and protect the platform against different forms of abuse.

This role will play a key part in building the company's risk intelligence by transforming large volumes of data into automated decisions that reduce financial losses while preserving the experience of legitimate users.

You will work closely with the Anti-Fraud, Product, Engineering, Analytics, and Payments teams to develop scalable fraud prevention solutions, ranging from exploratory analyses to Machine Learning models and real-time decision engines.

• Develop Machine Learning models for fraud prevention and abusive behavior detection.
• Build Behavioral Risk Scoring algorithms using behavioral, transactional, and contextual signals.
• Design intelligent blocking rules and automated decision strategies to reduce fraud without compromising the experience of legitimate users.
• Develop models to detect promotional fraud, consumer abuse, payment fraud, multi-accounting, and other forms of platform abuse.
• Identify new fraud prevention opportunities by analyzing user behavior patterns.
• Build and continuously improve features that enhance the predictive power of models.
• Design and run experiments and A/B tests to evaluate new fraud prevention strategies.
• Continuously monitor the performance of deployed models and fraud rules.
• Partner with Engineering to deploy models into production and ensure their scalability.
• Collaborate with Data Engineering to ensure the quality and reliability of the data used by the models.
• Develop dashboards and metrics to track key fraud KPIs.
• Support the Anti-Fraud and Product teams in decision-making through quantitative analysis.

• Experience applying Data Science to solve complex business problems.
• Hands-on experience deploying and maintaining Machine Learning models in production environments.
• Advanced proficiency in Python and SQL.
• Experience with Feature Engineering and predictive modeling.
• Strong knowledge of supervised and unsupervised learning algorithms.
• Experience with libraries such as Scikit-learn, XGBoost, LightGBM, or similar frameworks.
• Knowledge of model experimentation, validation, and monitoring.
• Experience working with large-scale datasets.
• Ability to translate business problems into analytical solutions.
• Strong communication skills and the ability to collaborate effectively with cross-functional teams.

• Experience in Anti-Fraud, Payments, Trust & Safety, or Risk.
• Knowledge of real-time decision systems.
• Experience with Behavioral Analytics.
• Experience with anomaly detection.
• Knowledge of Graph Analytics or Graph Machine Learning.
• Experience in marketplaces, fintechs, delivery platforms, or payment companies.
• Experience applying Generative AI to fraud prevention.

• Strong analytical skills and curiosity to investigate behavioral patterns.
• A hands-on, problem-solving mindset.
• Critical thinking and the ability to propose innovative fraud prevention strategies.
• High level of autonomy and a strong sense of ownership.
• Passion for building scalable solutions with direct business impact.
• A data-driven mindset with a focus on continuous improvement.
• Ability to balance security, conversion, and user experience.