Senior Data Scientist
We are looking for a Senior highly technical Data Scientist to join our established Analytics department. This is a modeling-heavy role focused on building high-performance, reproducible machine learning systems that drive core business decisions. You will join the newly formed AI Lab which is entrusted with growing our AI/ML capabilities at IDT.
We are looking for a veteran modeling expert who thrives on building novel ML architectures from the ground up for business functions like fraud detection, customer engagement, process performance and finance.
Responsibilities:
- Advanced ML R&D: Design, develop, and maintain cutting-edge, custom machine learning models for production environments.
- Behavioral Forecasting: Build advanced models to predict bad actors in our transaction flow.
- End-to-End Modeling: Develop and implement both supervised and unsupervised models from scratch to find anomalies and next likely outcome.
- Content Generation: Design generative models based on profile and transaction data.
- Production Deployment: Deploy models into product in a real-time environment.
- Interact with MLOps functions to maintain models and increase accuracy, recall and precision.
- Experimental Design: Lead the statistical design and analysis of A/B testing to validate model performance and business hypotheses.
- 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.
Requirements
Experience: 5+ years of professional experience in Data Science, with a strong portfolio of building and shipping original ML models. Deep theoretical and practical understanding of supervised/unsupervised learning, including Boosting, NN, Bayesian, Clustering, and related frameworks.
Core Stack: Python: Advanced use of Pandas, Numpy, PyTorch/TensorFlow, and Scikit-learn for complex feature engineering, custom model design, and pipeline optimization. SQL: Proficiency in querying and structuring data from large-scale databases. MLOps: Experience using MLOps tools like MLflow or ClearML in model development and inference.
Education: Bachelor’s degree in a quantitative field (Computer Science, Statistics, Mathematics, or related).
