This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Data Scientist Sênior (Prevenção a Fraudes) based in Brazil.
As a Senior Data Scientist, you will help build and scale Machine Learning solutions designed to prevent transactional financial fraud.
You will own Data Science initiatives end to end, from framing business problems to deploying and sustaining models in production.
The role combines advanced analytics, real-time inference, MLOps, and large-scale data processing in high-volume environments.
You will optimize models and infrastructure for reliability, low latency, scalability, and measurable business impact.
Close collaboration with Product, Operations, Engineering, and technical leadership will be essential to turn analytical solutions into production capabilities.
You will also contribute to stronger model monitoring, experimentation, observability, and continuous improvement practices.
This is a fully remote opportunity for someone who wants to solve complex fraud challenges in a collaborative, fast-moving Data & AI environment.
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.
How Jobgether works:
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
We appreciate your interest and wish you the best!
Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.
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