Lead the entire machine learning model lifecycle, from initial research and hypothesis testing to production deployment and maintenance
Translate complex business goals into well-defined data science problems and quantifiable metrics
Design and develop robust, scalable machine learning systems from scratch, including data analysis, annotation, and processing pipelines
Contribute to the overall system architecture and integrate ML models with existing backend services and infrastructure
Monitor and maintain deployed models, proactively identifying and addressing issues like concept drift to ensure consistent performance
Support the development and growth of other team members through mentorship and participation in onboarding programs
Drive continuous improvement by automating repetitive tasks and proposing innovative solutions that lead to significant business impact
Communicate complex technical concepts and findings clearly and concisely to both technical and non-technical stakeholders
Qualifications
Previous experience in a data science or machine learning role
An academic background in a quantitative field such as Computer Science, Mathematics, or a related discipline will be a plus
Expert-level proficiency in Python and its core data science libraries (e.g., Pandas, NumPy, Scikit-learn, PyTorch)
Deep expertise in classic machine learning and deep learning techniques, with a strong understanding of advanced mathematics relevant to these fields
Experience with ML system design and MLOps practices for building, testing, deploying, and monitoring models in a production environment
Proven experience with event systems, deployment environments, and maintaining production services
Familiarity with technologies for streaming, batch, and async data processing. Proficiency in at least one specialized ML domain (e.g., NLP, Computer Vision, Tabular ML, Graph Neural Networks)
Strong understanding of software system design principles and the ability to contribute to architectural discussions
Experience in experimental design to validate hypotheses and measure the effectiveness of solutions
A solid grasp of security, risk, and control concepts in a production environment
Conditions & Benefits
Stable salary, official employment
Health insurance
Hybrid work mode and flexible schedule
Relocation package offered for candidates from other regions
Access to professional counseling services including psychological, financial, and legal support
Discount club membership
Diverse internal training programs
Partially or fully payed additional training courses