Staff Data Scientist

Responsibilities

ML system design from scratch with data analysis, annotation, and processing in several areas of research or in multimodal scenarios
Lead the process of data preparation and calculations with master systems on all stages
Translate business level metrics to an engineering/science problem
Take ownership of the end to end DS product lifecycle, including ML model pipelines, implementation, A/B testing, and rollout
Develop innovative services designed to enhance the efficiency and precision of recommendation techniques and outcome estimation
Actively use modern AI approaches for building user and behavioral profiles
Apply state-of-the-art methods including deep learning, LLMs, and multimodal techniques where appropriate to solve real-world problems.

Bachelor’s degree in Statistics, Mathematics, Computer Science, Machine Learning
5+ years of industry experience as an ML Engineer, Applied or Data Scientist or equivalent
Proficient in MLOps instruments to organize ML model lifecycle
Has common knowledge of technologies from Company Tech Radar to integrate with existing backend systems and infrastructure
Has experience in event systems, deployment environments etc. to be able to maintain DS powered services in production
Proficient Python and in Python frameworks for streaming, batch and async data processing
Subject-matter expert in more than one ML domains (e.g. RecSys, CV, NLP, time-series forecasting etc.) with a demonstrated ability to deploy solutions at scale
Proficient in LLM usage both for solving technical and business problems
Nice to have experience with different domains of knowledge on different topics, preferable in Food delivery, Ride-haling or Advertising areas

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 paid additional training courses
All necessary work equipment