Data Scientist

Overview of the role

In Data Science we work with the data from fast changing consumer goods world. Data comes both from standardized databases and from online retail channels in various forms like semi-structured text in various languages, images or precise numeric values.

Currently we are looking for a new colleague to join the team and assist developing new and improving existing ML models according to our client feedback and suggest new ways looking at the data which would result in new product

Role & responsibilities

What youll do

• Formulating hypotheses and developing proof-of-concept ML models for NLP, Image Processing,
• Supervised/Unsupervised Learning tasks
• Translating Industry specific knowledge into proper models
• Monitoring and ensuring data quality
• Communicating insights effectively in both written and visual way

What were looking for

• Advanced knowledge of Python, understanding of merge-request git flow, basic scripting including CI/CD, regular expressions, shell commands and basic webapp development skills.
• Background in Mathematics/Physics/Natural Sciences or Engineering fields, relevant working experience of 2+ years
• Good coding habits such as proper documentation, linting, styling, reproducibility, code review best practices, and test-driven development.
• Skills to maintain ML models: measure performance, add/remove classes, create effective labelling batches, retrain with assessment of the impact.
• Ability to suggest model improvements, propose working solutions for business problems, ensure the model works with existing models, track model experiments and keep model documentation updated.
• Skills to clearly communicate problem status, actions taken, conclusions, improvements and limitations.
• Understanding of which visualizations to use with different data types, comfortable with at least one visualization framework, and capable of creating shareable/dynamic reports/apps.
• Proficiency in applying EDA principles, classical supervised/unsupervised learning techniques, main NLP techniques, a good understanding of deep-learning techniques and awareness of model and computational complexity.