We are growing our ML Solutions squad inside the Global AI department to help build our MLOps platform and scale our usage of machine learning at HelloFresh. The Global AI team works cross-functionally to identify opportunities to build and deploy advanced ML solutions throughout the organization and works closely with Data Scientists, ML engineers, and Data Engineers to build and maintain best-in-class data products to improve HelloFresh’s user experience. The Global AI team is focused on designing scalable and automated software and ML pipelines using a variety of AI, big data tools, and cloud platforms (AWS, Spark, Prefect, Databricks, etc…)
## **What you'll do**
- Develop and improve Machine Learning solutions to help our data scientist productionize models across the full customer experience (web, marketing, logistics, …)
- Enable data scientists to easily provision clusters to train models on huge datasets and deploy feature engineering pipelines.
- Deploy and monitor models in production (e.g. building artifacts, measuring model drift, integrating with CI/CD, etc ...)
- Support and consult with data scientists regularly to ensure a high adoption rate of our solutions across HelloFresh and promote best practices
- Close the loop with human interaction: automating measurements and feedback gathering to iterate on our solutions
## **What you'll bring**
- Experience deploying ML models to production
- Previous commercial experience building machine learning models or developing MLOps solutions
- Knowledge of basic modeling techniques for classification, regression, or time-series forecasting (e.g. Logistic Regression, Regularization, Gradient Boosting, Random Forests, etc…).
- Demonstrated experience building software with Python and proficiency with data and ML-related open-source libraries such as Pandas, Scikit-Learn, Catboost/XGboost, TensorFlow.
- Demonstrated experience implementing good software engineering practices (e.g. version control, code modularity, testing, …)
- Experience working with Cloud infrastructure (e.g. AWS, GCP, Azure)
- Experience with Infrastructure as Code (e.g. Terraform or similar)
- Experience with Deep Learning techniques is a plus
- Experience with Apache Spark is a plus
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