Continuous collaboration with colleagues from various business units and clients
Exploration, analysis, and evaluation of various data sources to identify the most appropriate data
Selection of the most suitable models and training them
Conducting analysis of the business and technical impact of proposed solutions
Collaborating with the team to implement developed solutions in production environments
Developing analytical, user, and technical documentation
requirements-expected :
At least 3 years of experience in a Senior/Expert/Lead Data Scientist role or similar
Higher education degree (Masters or Doctorate) in Data Science, Statistics, Computer Science, Mathematics, Econometrics, or other related quantitative fields position
Statistical and Machine Learning Expertise: high knowledge and extensive experience in statistical and machine learning data modeling
Programming and Data Processing: strong proficiency in programming in Python and/or R programming, SQL, and familiarity with data processing libraries such as dplyr, Pandas, Polar, data.table
Data Modeling Expertise: advanced skills in selecting optimal data modeling methods for various business challenges, including diverse datasets such as tabular, time-series, language, or image data
Statistical Methods Knowledge: good understanding of generalized linear models, with an emphasis on logistic regression
Tree-Based Models Proficiency: advanced experience in creating and working with tree-based models, including boosted models using libraries like lightGBM, GBM, CatBoost, or XGBoost
Neural Networks Skills: advanced ability to create and work with neural network models, including the use of libraries such as Keras, TensorFlow, PyTorch, or Theano
GPU Model Management: experience in managing and training/finetuning (NLP, GenAI, or CV) models with GPU resources (on-premises and/or cloud)
Data Visualization: high practical skills in data visualization, including proficiency in at least one library: seaborn, matplotlib, or ggplot2
Presentation Skills: excellent skills in presenting materials to both business and technical audiences
Containerization and API Technologies: experience with container technologies (Docker/Kubernetes) and API services
Agile Methodologies: familiarity with Agile methodologies (Agile, Scrum)
Version Control and Collaboration: knowledge of version control and code collaboration principles (Git), focusing on reproducibility and accountability in analysis
Adaptability: flexibility and willingness to learn business knowledge
Teaching Ability: aptitude for teaching and eagerness to share knowledge and promote a Data Science culture within the organization
benefits :
sharing the costs of sports activities
private medical care
sharing the costs of foreign language classes
sharing the costs of professional training & courses
