ML Engineer

Job Overview

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.

Key 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.

Required Qualifications

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, track model experiments and keep model documentation updated.

Skills to clearly communicate problem status, actions taken, conclusions, improvements and limitations.

Experience creating and using cloud resources, familiarity with notebooks, SQL instances, buckets, secrets, VMs, and service accounts, understanding and contributing to pipelines, applying model scaling practices and cloud cost awareness.

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.

Benefits

Hybrid working model (2 days per week in office)

Exposure to International, high impact projects

Work for a hugely global organisation

Flexible working hours

Work from anywhere in the world for up to 3 weeks per year