Data Science Specialist

Own the end-to-end design, development, evaluation, and deployment of Machine Learning and AI solutions, ensuring scalability, reliability, and performance;

Develop and optimize Machine Learning models, with a strong focus on classical Machine Learning techniques such as XGBoost, Random Forest, and Decision Trees, ensuring robustness, explainability, and efficiency;

Build models and strategies focused on fraud prevention, chargeback risk, and risk prevention in credit card payments;

Translate business and payment-related challenges into effective Machine Learning solutions, considering both technical performance and business impact;

Build and maintain production-grade solutions for AI applications, including APIs, data pipelines, and services, in collaboration with AI Engineering and MLOps specialists;

Implement and oversee continuous integration and continuous delivery (CI/CD) pipelines for AI model deployment and updates;

Collaborate closely with data scientists, AI engineers, MLOps specialists, software engineers, and stakeholders across teams to translate business requirements into actionable AI solutions;

Troubleshoot production issues related to AI and Machine Learning systems, performing rigorous testing, validation, and continuous improvement;

Contribute to the evolution of AI-powered solutions and explore new ways of applying AI to improve development efficiency and business outcomes;

Mentor team members on best practices in Machine Learning modeling, AI engineering, model deployment, and system integration.

Must-haves to shine in this role:

Hands-on experience in Data Science and Machine Learning modeling;

Proven expertise in developing and evaluating Machine Learning models, especially using XGBoost, Random Forest, and Decision Trees;

Strong programming skills in Python, with demonstrated experience in developing high-quality, maintainable code;

Experience working with Machine Learning models in real-world or production environments;

Strong analytical and problem-solving skills, with the ability to translate business challenges into Machine Learning solutions;

Proficiency with cloud platforms (AWS, GCP or Azure), including deploying AI services and managing cloud resources;

Strong knowledge of CI/CD tools and processes (GitHub Actions);

Solid understanding of data engineering concepts, ETL processes, and data pipeline architectures;

Experience with monitoring and logging solutions for ML systems (e.g., CloudWatch or DataDog);

Awareness of security standards and compliance requirements relevant to AI and data-driven systems;

Conversational English proficiency, with the ability to communicate effectively with international stakeholders.

Bonus points if you have:

Prior experience in fraud prevention, fraud detection, chargeback, payment risk, financial services, fintech, or related industries;

Experience developing or working with neural networks and other Machine Learning techniques;

Experience with containerization tools (Docker);

Experience collaborating closely with MLOps and AI Engineering teams;

Bachelor’s or Master’s degree in Computer Science, Engineering, Artificial Intelligence, or a related field;