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22 hours ago
As a data engineer, you will be responsible for designing and building scalable data pipelines to transform raw events into actionable insights. Your expertise in Python, SQL, and machine learning libraries such as scikit-learn, LightGBM, and XGBoost will be crucial in defining and computing labels from historical events.
You will work closely with backend engineers to integrate model outputs into production systems, ensuring seamless collaboration across teams. Additionally, you will be responsible for setting up retraining and monitoring pipelines using tools like Airflow and MLflow.
Key Requirements:- 3–5+ years of experience in data engineering or applied machine learning.
- Strong proficiency in Python, SQL, and one or more machine learning libraries.
- Experience with data pipelines, including ETL and event-driven systems.
- Understanding of the machine learning lifecycle, including training, serving, monitoring, and retraining.
- Ability to design time-based labels and solve complex problems.
The ideal candidate will have a strong problem-solving skills and be eager to work in a fast-paced startup environment. Experience with MLOps tools, bandit algorithms, A/B testing, or uplift modeling is a plus.
Nice-to-Haves:- MLOps tools and experience with customer engagement platforms.
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