Careers
ML Engineer/Data Scientist
We are looking for ML Engineer in our financial area. You will have to analyse user behavior, improve product performance using ML models, and reduce the risk of losses.
Here are some more facts that may interest you:
— The projects are at an early stage, so there is an opportunity to greatly influence their development;
— Here everyone can influence the course of the project, test their wildest hypotheses and use advanced technologies;
— There are many potential uses for ML in the area; it will be possible to build up skills and explore new business domains.
Future challenges
- Construction, deployment, testing and support of ML models for analyzing and predicting user actions in the product and key business metrics;
- Assessing the impact of the project on the company’s key business metrics;
- Hypothesis formulation to improve metrics and hypothesis testing;
- Participation in the formation of the project development plan.
We expect
- Deep knowledge of classical Machine Learning algorithms, methods for assessing the quality of models, understanding of the basic principles of DL;
- Basic skills in designing ML systems, considering the requirements for scalability, performance, accuracy and other SLAs;
- Knowledge of SQL at the window function level;
- Knowledge of mathematics (basics of mathematical analysis, probability theory, statistics);
- Confident knowledge of Python and main libraries (Pandas, NumPy, Scikit-Learn, Plotly, Catboost, FastAPI);
- Confident knowledge of Unix, Git, Docker.
Advantages
- Experience in delivering models to production;
- Experience with Gitlab CI pipelines;
- Monitoring of production models (Grafana, ELK), monitoring of metrics;
- Conducting A/B tests to verify hypotheses.
We offer
- Exposure to international markets and projects;
- Support for your professional growth through seminars, courses, and training;
- Competitive compensation package and official employment;
- Health insurance coverage within your first two weeks (including family members);
- Flexible work hours to fit your schedule;
- Hybrid work model (2 days in the office, 3 days remote);
- On-site perks including delicious lunches and coffee points;
- English language training with a corporate tutor;
- Partial compensation for sports and wellness activities;
- Choice of work equipment;
- Relocation package.
