Junior Engineer
Artefact · London
Who we are
- 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.
What you will be doing
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.
Qualifications
Necessary education and experience
- 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.
Desirable experience
- 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