Senior Data Scientist

Key Responsibilities:

ML & Predictive Modeling: Design and build new models from the ground up (gradient boosting, neural networks, ensembles, anomaly detection) for anti-fraud, risk assessment, and personalization; maintain rigorous validation practices to prevent data leakage and overfitting.

End-to-End & MLOps: Partner with Data Engineering and MLOps to operationalize models in production (batch and real-time/low-latency); build training, experiment tracking (MLflow), and model/data drift monitoring pipelines.

Experimentation & Business Impact: Design A/B tests and statistical evaluations to measure model performance against key business metrics (reduced fraud losses, transaction acceptance rates, decision latency).

R&D & Technical Leadership: Explore emerging techniques (LLMs/GenAI, graph-based methods), establish engineering best practices, and mentor fellow team members.

Requirements (Must-Have):

Experience: 5+ years of hands-on experience in Data Science / ML, with a strong focus on designing and building new ML models from scratch and delivering them into production with measurable business impact.

English: B2+ (Upper-Intermediate or higher) — fluent written and spoken communication for technical discussions and daily collaboration with an international team.

Math & Modeling Foundations: Deep understanding of statistics, probability, and validation methodologies; practical experience handling class imbalance, model calibration, and anomaly detection.

Tech Stack:

Python: Advanced proficiency (Scikit-learn, Pandas, NumPy);

Deep Learning: Hands-on experience with PyTorch or TensorFlow;

SQL: Complex queries and large-scale data manipulation;

MLOps Tools: Experiment tracking and model lifecycle management (MLflow, ClearML, etc.).

Education: Bachelor’s degree in Computer Science, Mathematics, Statistics, Engineering, or a related quantitative field.

Nice-to-Have:

Domain experience in FinTech, Payments, or Fraud Prevention: real-time transaction scoring and low-latency inference.

Experience with cloud data platforms (Snowflake), Feature Stores, and event-driven architectures.

Background in time-series forecasting, graph neural networks, Explainable AI (XAI), or applied LLM solutions.

Master’s degree or Ph.D. in a quantitative discipline.

We offer you:

Remote b2b or hybrid (Belarus-Moldova) work opportunity!
Stable job with long-term growth perspective.
Competitive salary with annual performance review.
Really good hardware.
An exciting and challenging job with talented people around.
Continuous learning and career growth opportunities.
Compensation for professional training, seminars, and conferences.
Referral program – get rewarded for helping us grow the team with talented people.
Company-supported English classes to enhance your professional growth.
More perks for the Minsk and Chisinau office employees.