Senior Data Scientist

We are looking for a Senior Data Scientist to join our team. This job is about turning data into actionable knowledge, which requires a blend of scientific, problem-solving, analytical, technical, and communication skills. The role is primarily focused on (but not limited to) solving business problems in the technological domain of Data Science. Responsibilities Identify business problems, find the right data and methods to address them Process large volumes of structured and unstructured customer data using advanced analytical solutions Turn analyzed data into unique actionable insights and business value Create high-quality data visualizations Create data pipelines for feature engineering, missing values treatment, outliers' detection, etc Perform research, build, and validate analytical, mathematical, econometric, descriptive, and predictive models that deliver better decisions across a broad range of industries and data sources Estimate, plan and coordinate the delivery of moderate/large sized projects Evaluate and select the best matching Data Science tools and libraries Present findings in a clear and informative way Communicate effectively with different departments and roles (stakeholders, product managers, engineers) to discuss complex data-driven findings and technical matters Continuously develop knowledge base within the Data Scientists team, participate in common brainstorming sessions Educate junior and middle Data Scientists Requirements 3+ years of relevant experience working with a variety of information systems and data Writing high-quality Python code according to PEP-8 guidelines Strong knowledge of various data exploration and analysis techniques, ability to build models or analytical frameworks, develop hypotheses, and design experiments Advanced knowledge in one of the domains: Computer Vision, Time Series, Natural Language Processing, Recommendation Systems, Predictive Analytics Knowledge of EDA, classical vs advanced Machine Learning models, model performance metrics and losses, model fine-tuning Knowledge of pros&cons for SOTA ML architectures and applicable use cases for them Understanding of ML projects' life cycle and ability to follow methodology processes (CRISP-DM, TDSP, LeanDS, etc.) used in data science projects Ability to design and supervise data labeling process Ability to interpret and report results of analyses, provide business recommendations; ability to coach and train others Nice to have Strong academic background, Bachelor's / Master's Degree in Computer Science, Math, Applied Statistics, or a related field Machine Learning Engineering mindset, experience with Docker, Linux, CI/CD, MLflow, etc Projects completed using different methodology/models or knowledge of one of the Deep Learning frameworks (PyTorch, TensorFlow) Experience with ML services of cloud providers: AWS, Azure, or GCP Experience with PySpark Some experience in ML project technical leadership