Data Scientist Sênior (Prevenção a Fraudes)

Accountabilities • Lead Data Science projects end to end, from problem definition and scoping with Product and Operations through production deployment, impact measurement, and continuous improvement.

• Design, implement, and maintain reliable, scalable Machine Learning pipelines for both batch and online processing in high-volume, low-latency environments.

• Manage the complete model lifecycle, including experimentation, validation, deployment, monitoring, and iteration, while defining metrics and routines for detecting data drift and concept drift.

• Develop and evolve inference systems for batch and real-time use cases, making architectural trade-offs to support concurrency, parallelization, scalability, and low latency.

• Optimize production solutions end to end, improving latency, throughput, and computational resource utilization to support rapid responses and multiple simultaneous requests.

• Establish and improve MLOps practices covering infrastructure, deployment, observability, model and artifact versioning, automated releases, and continuous monitoring of system and model health.

• Partner with Engineering Tech Leads and Product Managers to design viable solutions, align expectations, communicate technical and business results, and enable successful production delivery.

• Stay current with Data Science best practices and modern development productivity approaches, including AI-assisted development tools, identifying improvements that enhance delivery quality and efficiency.

Requirements

• Strong expertise in Machine Learning algorithms for classification, regression, and risk scoring, with practical experience using methods such as  XGBoost, LightGBM, and Random Forest .

• Proven experience deploying and maintaining Machine Learning models in both live and batch production environments.

• Hands-on experience with real-time model serving through APIs, including high-concurrency scenarios and low-latency requirements.

• Solid MLOps experience, including the management and monitoring of production models and familiarity with frameworks such as  MLflow .

• Strong proficiency in  Python, SQL, and Spark  for analyzing and processing large datasets.

• Knowledge of statistical inference and practical experience applying  A/B testing  to business problems.

• Familiarity with structured software development practices and code versioning using  Git .

• Strong business acumen and communication skills, with the ability to translate statistical and technical results into clear, actionable business impact.

• Bachelor's degree or equivalent practical experience in Computer Science, Engineering, Statistics, Mathematics, Physics, or a related field.

• Experience with  Databricks , including Data Asset Bundles, Model Serving, and Online Features with DynamoDB, is a plus.

• Previous experience in fintech, payments, financial services, fraud prevention, or financial crime is desirable.

• Knowledge of fraud-related financial metrics such as approval rates and chargebacks is an advantage.

• Familiarity with streaming technologies such as  Kafka  or  Kinesis  and real-time processing is a plus.

• Knowledge of Graph Data Science or graph databases such as  Neo4j  or  AWS Neptune  is desirable.

• Familiarity with  AWS  is an advantage.

Benefits

• CLT employment contract  with an 8-hour workday, Monday through Friday.

• 100% remote work model  with support for a productive home-office setup.

• Home-office allowance and work equipment.

• Furniture allowance and access to  WOBA  coworking spaces across Brazil.

• Medical and dental insurance with no coparticipation.

• Life insurance.

• Medication assistance and support for physical activities.

• Flexible food allowance through a Visa credit card.

• Childcare assistance and parental support programs.

• Extended maternity and paternity leave.

• Access to a corporate training platform and continuous development opportunities.

• Education assistance covering 70% of eligible undergraduate and language program costs, as well as selected courses and books.

• Four free monthly therapy or nutrition sessions through a wellbeing platform.

• Financial wellbeing support.

• Day off during your birthday month.

• Annual performance-based bonus.

• Referral bonus program.

• Happy Hour allowance.

• Stock Options plan.

• Quick massage available at the headquarters.

• Flexible, informal work environment with no dress code.

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