Accelerate the development, deployment, and monitoring of machine learning models that drive marketing optimization at HelloFresh.
Requirements
- Minimum 4 years of commercial experience building machine learning models.
- Advanced knowledge of models for classification, regression, and time-series forecasting (e.g. Gradient Boosting, Random Forests, Lasso Regression, Prophet, etc.).
- Experience deploying ML to production.
- Demonstrated experience building software with Python and proficiency with data and ML-related open-source libraries such as Pandas, Scikit-Learn, XGBoost/CatBoost, TensorFlow, PyTorch.
- 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 Apache Spark.
Responsibilities
- Build tooling and automation that enables other engineers and scientists on our squad to develop and test machine learning products (e.g. pricing models, reinforcement learning, recommender systems, propensity models, etc.).
- Build end-to-end pipelines comprising data collection, feature engineering, model training, model evaluation, model scoring, and deployment.
- Deploy and monitor models in production (e.g. building artifacts, measuring model drift, integrating with CI/CD, etc.).
- Support and consult data scientists regularly to ensure successful adoption of our ML tooling.
Other
- A proactive, solution-oriented contributor in an agile, engineering-driven team, working closely with product managers, data scientists, and business intelligence partners.
- Eager to bridge the gap between experimentation and production—turning research into impactful, real-world products.
- Invested in understanding HelloFresh’s core business and customer experience, so you can help deliver value where it matters most.
- BSc or MSc in STEM discipline with relevant commercial experience.
- Experience with deep learning techniques is a plus.
