Data Scientist - ML Engineering

What impact do you have?

Design, prototype and develop machine learning systems, frameworks, pipelines and tools that support end-to-end predictive analytics projects and activities (training, inference & evaluation)

Partner with data specialists to deliver initiatives and solve complex data problems efficiently

Propose, validate and iterate on ideas that can significantly improve ML solutions, address system constraints, and optimise compute costs

Develop performance metrics to track the performance of new ML solutions and models

Drive continuous improvement activities incl. processes, documentation and training material

Remain abreast of latest developments and research in the ML field

What do you offer?

Basic or further education completed

in computer science, business informatics, data science, statistics or a related field

Work experience: 3-5 years

as a Data Scientist Machine Learning Engineering

English (business fluent)

Experience deploying data science solutions in cloud environments e.g. GCP, Azure, etc.

Strong knowledge of ML related technologies and libraries (Azure ML, Python, Pandas, Numpy, SciKit-Learn, Keras, PyTorch, TensorFlow, CNTK, SparkML, etc.)

Understanding of continuous integration, testing, deployment & release methodologies

Experience with Databricks, Keras, TensorFlow, PyTorch or similar tools for data automation, and model tracking, and training

Proficiency in Python and other coding languages such as Scala, Java, R, and C++

Strong proficiency in T-SQL/SQL/Spark SQL as well as version control software such as Git, BitBucket, CodeCommit or similar

Ability to work independently and effectively across projects while managing conflicting priorities