While the data scientists spend time understanding the business, prototyping algorithms, concerning about the statistical aspects of the model, ML Engineer take the prototype requirements and optimize the implementation
Ensure that ML models are implemented in the most efficient manner, so that the application can run smoothly and be easily understood in case of handover/bugs
Improve the efficiency that the data scientists by building reusable pieces of code to make data science work more efficient
Lead the way of setting coding standards and following the teammates so that the process gets better each time
Partner with IT to define ML model monitoring
Profile description:
Degree in computer science with emphasis in machine learning or equivalent in experience
Strong Experience in Python, R and Spark (Pyspark is desirable)
Deployment of ML models in production. Industry experience in agile development and DevOps (Docker, Kubernetes
Databases (ex. Oracle, AWS S3)
Big Data platforms (ex. Databricks, GCP, Cloudera)
Soft skills include working well in teams, dealing with uncertainty and ambiguity, flexibility