Your space-enabled career begins here

Key Responsibilities

• 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
  
  

Skills, Knowledge and Expertise

• 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.
• Access to professional counseling services including psychological, financial, and legal support.
• Discount club membership.
• Diverse internal training programs.
• Partially or fully paid additional training courses.
• All necessary work equipment.