Junior Research Scientist (Risk Team)
Summary
Entry-level research scientist training self-supervised foundation models on financial data (bureau, transactional, graph) for credit risk, anti-fraud, acquisition and LTV tasks. Day-to-day: experiments and training runs in Python/PyTorch with MLflow, AWS and Snowflake, plus SOTA research and evaluations. Remote worldwide within GMT-2..GMT+5, with relocation support to hubs.
We are looking for a Junior Research Scientist to work on foundation models for financial data. Several of them already run in production on bureau, transactional and graph data; your work is to extend that line to new data domains and new downstream tasks — credit risk, acquisition, anti-fraud, LTV forecasting.
We work with Python, PyTorch, MLFlow, AWS, Snowflake and modern AI coding tools such as Claude Code, OpenAI Codex and Cursor.
This is a great opportunity for someone at the beginning of their career and who wants to work on large-scale, technically challenging projects.
Challenges that await you
- Train self-supervised models on discrete sequences to beat the SOTA and achieve business impact in downstream tasks across Plata, such as: credit risk, transaction anti-fraud, acquisition and LTV forecasting.
- Stay on top of SOTA research, applying the latest NLP and DL techniques to fintech models.
- Work with large multimodal datasets: tabular, behavioral, transactional, device and network, text, time series, graphs.
- Optimize the utilization of compute resources for both training and inference.
- Own and develop solutions end-to-end, from idea to data collection to experiments to training runs to inference optimization to evaluating impact.
- Perform rigorous evaluations.
- Write articles and speak at industry conferences.
What makes you a great fit
- Education: mathematics, engineering, computer science, artificial intelligence or another strong quantitative field.
- Strong foundation in machine learning and deep learning, with hands-on experience building and experimenting with models using PyTorch and LLMs.
- Understanding of dataset design and data mixing for deep learning training.
- Demonstrated understanding of how to maximize the utilization of compute resources and efficiently perform experiments to validate research ideas.
- Solid communication skills.
- High autonomy and ownership.
Your bonus skills
- Published research in machine learning or deep learning.
- Personal projects or startup experience in AI, machine learning or deep learning.
- Internship experience in research, ML or DL teams.
- B1 or higher English level for effective communication with an international team.
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 dare to challenge the status quo of incumbent finances.
Our benefits
- Relocation support to one of our hubs — Serbia, Georgia, Mexico or Colombia — with assistance for the employee and their family.
- Flexible work from one of our offices or remotely within time zones from GMT-2 to GMT+5.
- 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