ML Engineer Europe

As a Machine Learning Engineer at the global jewelry company, you will play a key role in developing and deploying machine learning models that drive impactful decisions and elevate customer experiences. You will collaborate closely with a diverse group of data scientists, data engineers, and business leaders to create scalable solutions that optimize our retail operations across the full value chain – from jewellery design and crafting to supply chain logistics, to in‑store/online retail and marketing.

We encourage creative problem‑solving, continuous learning, and work‑life balance in a supportive environment.

Work in end‑to‑end analytical products, from exploration and prototyping to productizing and testing, helping to solve complex business problems across the full value chain.

Develop and maintain data pipelines to collect, process, and analyze large datasets, ensuring data quality and consistency.

Deploy machine learning models into production environments, ensuring they are scalable, reliable, and easy to maintain.

Work closely with cross‑functional teams, including data scientists, software engineers, MLOps engineers, product managers, and business stakeholders, to understand business needs and translate them into machine learning solutions.

Continuously monitor the performance of deployed models and implement improvements to enhance their accuracy, efficiency, and scalability.

Stay up to date with the latest developments in machine learning and AI, and apply new techniques to improve existing models and processes.

Maintain clear and comprehensive documentation of models, processes, and systems to facilitate knowledge sharing and collaboration.

Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Applied Mathematics, or a related field.

4-7 years of experience as a Machine Learning Engineer, Data Scientist, or similar role.

Experience in the retail industry or e‑commerce is highly desirable.

• Strong problem‑solving abilities, solid background in algorithms and data structures.
• Strong proficiency in Python.
• Ability to work with machine learning tools (e.g., scikit‑learn, Tensor Flow, Keras, PyTorch, Spark MLlib).
• Experience with big data using Databricks, Snowflake, Apache Spark, or Hadoop.
• Proficiency in SQL and experience with relational databases.
• System‑level architecture understanding, including scaling, MLOps, model/data monitoring, and ensuring a deterministic pipeline.
• Familiarity with cloud platforms (e.g., Azure) and containerization technologies (e.g., Docker, Kubernetes).
• Experience with version control systems (e.g., Git) and collaborative development tools (e.g., JIRA, Confluence).

• Strong problem‑solving skills with the ability to work independently and collaboratively in a fast‑paced environment.
• Excellent communication skills, with the ability to explain complex technical concepts to non‑technical stakeholders.
• A proactive attitude and a passion for continuous learning and innovation.