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3 days ago
At the forefront of innovation, we are evolving our platform to harness the power of artificial intelligence. Our customer engagement platform is revolutionizing the way businesses interact with their clients, providing a seamless and personalized experience.
Key Responsibilities:- Data Architecture Design: Develop and implement data pipelines to transform raw events into actionable insights.
- Label Engineering: Define and compute labels from historical events, ensuring accurate predictions.
- Model Development: Train and deploy ML models using Python (scikit-learn, LightGBM, XGBoost), driving business growth and improvement.
- Real-time Prediction Services: Build scalable prediction services to serve insights in production systems.
- Pipeline Monitoring and Retraining: Establish monitoring and retraining pipelines (Airflow, MLflow) for continuous improvement.
- Collaboration and Integration: Work closely with engineers to integrate model outputs into workflows.
- Quality Assurance: Ensure data quality, reproducibility, and compliance, safeguarding our customers' information.
- 3–5+ years of experience in data engineering or applied machine learning.
- Proficiency in Python, SQL, and one or more ML libraries (scikit-learn, LightGBM, XGBoost, PyTorch).
- Experience with data pipelines (Airflow, dbt, or custom ETL).
- Comfortable with event-driven systems (Kafka, Redis, ClickHouse or similar OLAP).
- Understanding of ML lifecycle: training, serving, monitoring, retraining.
- Ability to design time-based labels, avoiding data leakage.
- Strong problem-solving skills and eagerness to work in a fast-paced environment.
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