
Data Engineering Machine Learning Specialist
3 days ago
We're looking for a skilled professional to bridge the gap between data engineering and applied machine learning.
Key Responsibilities- Data Pipelines: Design and build pipelines to transform raw events into usable features.
- Label Definition: Define and compute labels from historical events.
- Model Development: Develop and train ML models using Python (scikit-learn, LightGBM, XGBoost).
- Inference Services: Build real-time inference services to serve predictions into production systems.
- Pipeline Management: Set up retraining and monitoring pipelines (Airflow, MLflow).
- Collaboration: Collaborate with backend engineers to integrate model outputs into workflows.
Must have skills include proficiency in Python, SQL, and one or more ML libraries. Experience with data pipelines and event-driven systems is also required. Additionally, understanding of the ML lifecycle and ability to design time-based labels are essential.
Requirements- 3–5+ years of experience in data engineering or applied ML.
- Strong problem-solving skills and eagerness to work in a startup environment.
- Ability to design and implement efficient data pipelines and machine learning models.
- MLOps tools (MLflow, BentoML, Ray Serve).
- Experience with bandit algorithms, A/B testing, or uplift modeling.
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