Responsibilities

• Take ownership of the end-to-end machine learning (ML) delivery cycle, including building, testing, deploying, and supporting solution components
• Lead the design of complex ML systems from scratch, considering architectural aspects, user needs, and non-functional requirements
• Transform business goals into data science (DS) problems and define relevant proxy metrics and non-functional requirements
• Discover and verify business scenarios that can be solved with technical tools and solutions, contributing significantly to the experiment design process
• Manage issues from root cause to resolution, providing feedback to improve engineering design and prevent future issues
• Create and maintain DS-powered services in a production environment, collaborating with other teams and contributing to the backend systems and infrastructure
• Drive automation and track performance and efficiency metrics
• Mentor and onboard junior team members, supporting a culture of continuous learning and best practices
• Communicate complex technical messages clearly and concisely to diverse audiences
• Proactively identify and report potential security, risk, and control issues
• Drive continuous improvement and innovation that leads to business impact

Qualifications

• Comprehensive experience autonomously implementing and leading ML projects, with a proven track record of successes and lessons learned
• Expert-level proficiency in classic machine learning, deep learning, and advanced mathematics
• Strong practical knowledge of MLOps instruments for managing the ML model lifecycle
• Solid software system design skills to contribute to overall architecture, and the ability to design ML systems from scratch
• In-depth experience with event systems and deployment environments, and the ability to maintain services in production
• Proficiency in Python and its frameworks for streaming, batch, and async data processing
• Common knowledge of technologies for backend integration (e.g., Golang)
• A strong grasp of concepts like Concept Drift and its impact on model performance in production
• A strong understanding of data preparation and calculations at all stages of the ML pipeline

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
• All necessary work equipment