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Machine Learning Engineer [Collections] – Plata Card
Job Description
We are a fast-growing digital bank with a strong track record in technology and data-driven decision making. Our Collections department is now starting to adopt machine learning to improve debt recovery strategies, customer engagement, and operational efficiency.
We’re looking for a hands-on specialist who will design, implement, and maintain production-ready ML models, while helping us establish the best practices and infrastructure for sustainable, scalable use of ML in the future.
This role is ideal for someone who thrives on applied, production-focused work. You’ll join at the exciting moment when we’re starting to use ML in our Collections processes — which means you’ll have the opportunity to shape how ML is done here from day one.
We’re looking for a hands-on specialist who will design, implement, and maintain production-ready ML models, while helping us establish the best practices and infrastructure for sustainable, scalable use of ML in the future.
This role is ideal for someone who thrives on applied, production-focused work. You’ll join at the exciting moment when we’re starting to use ML in our Collections processes — which means you’ll have the opportunity to shape how ML is done here from day one.
Challenges that await you:
- Develop and deploy production-ready ML models for segmentation, outreach optimization, and risk prediction
- Build and maintain data pipelines for model training, retraining, and monitoring
- Collaborate with engineers to integrate models into automated workflows and decision systems
- Define and implement best practices for ML in production (versioning, testing, monitoring)
- Translate business problems into data-driven solutions and communicate results clearly to stakeholders
What makes you a great fit:
- 2+ years of hands-on experience in applied ML with strong focus on classic algorithms (e.g., gradient boosting, decision trees, logistic regression)
- Proficiency in Python and ML libraries such as Scikit-learn, XGBoost, LightGBM
- Strong engineering background: clean, maintainable code, Git, CI/CD, automated testing.
- Proven track record of deploying ML solutions to production
- Solid foundation in statistics and probability theory
- Degree from one of: MSU, MIPT, HSE, MSTU, MEPhI, NES (or similar-level institutions)
- Participation in math, physics, economics, or CS olympiads is a plus
Nice-to-Have Skills:
- Experience with deep learning frameworks (e.g., PyTorch, TensorFlow)
- Familiarity with large language models (LLMs) and modern NLP techniques
- Knowledge of modern ML approaches such as embeddings, transformers, or reinforcement learning
- Exposure to cloud ML environments (AWS Sagemaker, GCP Vertex AI, Azure ML)
What We Value:
- Pragmatic approach — delivering working solutions that create measurable impact
- Ability to work in a growing ML environment with evolving processes
- Initiative in establishing ML workflows and tools that will scale with the business
Our ways of working:
- Innovative Spirit: a commitment to creativity and groundbreaking solutions
- Honest Feedback: valuing open, transparent communication
- Supportive Team: a strong, collaborative community
- Celebrating Achievements: recognizing our wins together
- High-Tech Environment: a team full of smart and revolutionary people who date to challenge the status quo of incumbent finances
Our benefits:
- Relocation support to Mexico with assistance for the employee and their family
- Flexible work: hybrid or full-time from our office in Mexico
- Healthcare Coverage
- Education Budget: Language lessons, professional training and certifications
- Wellness Budget: Mental health and fitness activity reimbursements
- Vacation policy: 20 days of annual leave and paid sick leave
