Role overview
- 3-5+ years of professional experience building, training, validating, and deploying ML solutions in production environments.
- Very good knowledge of Python programming, SQL, and Git.
- Experience in training and validating ML solutions (decision trees, neural nets, regression models).
- Ability to scale solutions according to infrastructure or business requirements.
- Good understanding of data lake / lakehouse architecture.
- Good knowledge of English (C1) (work in an international environment).
Preferred qualifications
- Professional experience with recommender systems and NLP.
- Previous experience in ecommerce data ecosystems.
- Professional experience with PySpark programming and the Databricks Lakehouse platform ecosystem.
- Experience in structured streaming and Scala programming.
- Familiarity with MLOps environments such as mlflow.
- Hybrid or remote work model
- Flexible start to your day
- Real benefits you’ll actually use
- Office vibes we enjoy
- Learning & development
- Eco in action
- Activities and Events
About the company
- Building and deploying data-driven and machine learning solutions for portal personalization and campaign ranking.
- Taking care of the whole machine learning process – verifying data quality, choosing optimal algorithms, feature engineering, model validation with correct metrics, and interpretability.
- Monitoring, maintaining, scaling, and updating existing data / ML pipelines.
- Building solutions following best software practices – clean code, testing, and automatic deployments.
- Working closely with data engineers, data scientists, and development teams in building our whole ML / data-driven infrastructure – reliable data pipelines and shared, clean data sources.
- Explaining and recommending optimal data-driven / ML solutions to both business stakeholders and developer teams.
- Sharing and improving ML / MLOps knowledge within the organization.
Tags & focus areas
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