Artefact

Junior Engineer

17th Floor, 5 Aldermanbury Square, London, EC2V 7HR · Work style not stated · Full-time

Posted over 6 months ago: check it’s still open

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About the role

Description from the employer’s posting. Check the employer’s page for the current version.

Artefact is a leading global consulting firm dedicated to accelerating the adoption of data and AI. We work with a variety of businesses, from supermarket chains, to private equity firms and telecoms; including Nissan, L'Oréal, Carrefour, WHSmith, Orange, Beiersdorf, BNP Paribas, and Samsung.
Our success stems from combining advanced data technologies, agile methods for quick delivery, and dedicated teams of data scientists, data engineers, business consultants, and data analysts.
Our 1,700 employees operate in 25 countries (Americas, Europe, Asia, Middle East, India, Africa) and we partner with 1,000+ clients.

As a Junior Software Engineer in our London office, your role will encompass:

working on engineering projects together with senior team members
training and certification to enhance your skills in software engineering, data science, and data architecture.

Show full description

Education: A Bachelor's or Master’s degree in Computer Science, Mathematics, Statistics, Physics, Engineering, or a related quantitative field.
Programming Proficiency: Strong programming skills in Python and a solid understanding of data structures and algorithms.
Software Engineering Fundamentals: A demonstrable understanding of version control and repository tools (Git, CI/CD) and a commitment to writing clean, well-documented code.
Data Skills: Experience with SQL for querying relational databases.
Problem-Solving Skills: An analytical mindset with the ability to break down complex problems into manageable steps.
Eagerness to Learn: A genuine passion for technology and a proactive attitude towards self-development and learning new skills, as demonstrated through projects or coursework.
Communication & Collaboration: Strong verbal and written communication skills, with the ability to work effectively in a team environment and learn from senior members.

Cloud & MLOps Exposure: Basic familiarity with a cloud platform (AWS, GCP, or Azure) and an awareness of concepts like containerization (Docker).
Core ML Knowledge: A good grasp of fundamental machine learning concepts, including supervised/unsupervised learning, model evaluation techniques, and feature engineering.
Advanced Education: A Master's degree or PhD in a relevant field is a strong plus.

Hybrid working pattern

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