Euromonitor

Market Research

MLEngineer

€36–50k ~AI est. Vilnius, Vilnius City Municipality, Lithuania
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Mid candidates.

The Brief

“ML Engineer at Euromonitor”

Industry & Context.

Market Research
Full Job Description

**About the role:** Within our Data Science teams here at Euromonitor we work with data collated from the ever-changing and dynamic world of consumer goods. The data we analyse/collect comes both from standardized databases and from online retail channels in various forms, such as semi-structured text, which can come in a variety of languages, images or precise numeric values. To date we have already developed 400+ ML models which help us to identify consumer good attributes, of which are clients work with us to understand and gain insights from. Currently we are looking for a new colleague to join the team and assist in developing new and improving existing ML models according to our clients changing needs. Within this role you will work cross-departmentally to execute on a wide array of projects that will suggest new ways of looking at the data to support our clients. **Job Responsibilities:** * Formulating hypotheses and developing proof-of-concept ML models for NLP i.e. image / text Processing. * Work on Supervised/Unsupervised Learning tasks. * Translate Industry specific knowledge into proper models. * Monitor and ensure data quality. * Communicate insights effectively in both a written and visual way. **Requirements** * A background in Mathematics/Physics/Natural Sciences or Engineering fields, with relevant working experience of 2+ years. * Advanced knowledge of Python/R/SQL, understanding of merge-request git flow, basic scripting including CI/CD, regular expressions, shell commands, HTML/JavaScript and basic webapp development skills. * 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, tra

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