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Join our team of machine learning experts to bring cutting-edge causal inference models and algorithms into production at scale.
Collaborate closely with data scientists to deploy, optimize, and maintain models that drive performance marketing, portfolio optimization, and large-scale ad automation.
You will build robust and scalable pipelines, ensuring these advanced solutions deliver actionable insights and measurable impact in real-world applications.
YOUR ROLE AT SIXT Model Deployment and Optimization: Work with data scientists to implement and deploy causal inference models for online marketing optimization and automation —including A/B testing, difference-in-differences, propensity score matching, and double machine learning—in production environments.
Build and Maintain ML Pipelines: Design, develop, and maintain end-to-end pipelines for deploying machine learning models, ensuring scalability, reliability, and seamless integration with existing systems.
Cross-Functional Collaboration: Collaborate with data scientists, product managers, and software engineers to transform experimental models into fully operational systems that drive business outcomes.
Monitoring and Performance Tuning: Continuously monitor deployed models for performance, latency, and accuracy.
Implement feedback loops and conduct regular updates to improve and adapt models to changing business needs.
Scalability and Automation: Automate repetitive tasks and build scalable infrastructure to handle large-scale data and high-throughput model serving.
Knowledge Sharing: Document best practices and deployment workflows.
Communicate technical implementation details and insights to technical and non-technical stakeholders to foster collaboration and understanding.
YOUR SKILLS MATTER Strong Foundations in Machine Learning Engineering: Experience in building, deploying, and maintaining ML models in production, with a focus on ensuring robustness and scalability.
Proficiency in Python and production-oriented ML frameworks (e.G., TensorFlow, PyTorch, Scikit-learn).
Experience with ML pipeline tools like MLflow, Airflow.
Strong understanding of REST APIs, microservices architecture, and containerization tools like Docker and Kubernetes.
Familiarity with causal inference models and techniques, including A/B testing, difference-in-differences, and propensity score matching, is a plus.
Cloud and Big Data Experience: Hands-on experience with cloud platforms (AWS) and their ML/AI services.
Knowledge of distributed systems and big data technologies, such as Spark, Dask, or Polars, as well as multi-processing techniques for efficient offline learning and batch processing