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2 weeks ago
As a Data/ML Engineer, you will be responsible for building and deploying machine learning models to drive business outcomes. You will work closely with the engineering team to design, develop, and deploy data pipelines, and collaborate with cross-functional teams to integrate model outputs into our workflows.
We're looking for an engineer who can bridge the gap between data engineering and applied machine learning, bringing together technical expertise and business acumen to deliver results-driven solutions.
- Design and build data pipelines to transform raw events (visits, purchases, campaigns) into usable features.
- Define and compute labels (e.g., churn, upsell, lead quality) from historical events.
- Develop and train ML models (e.g., churn prediction, upsell propensity, lead scoring) using Python (scikit-learn, LightGBM, XGBoost).
- Build real-time inference services to serve predictions into production systems.
- Set up retraining and monitoring pipelines (Airflow, MLflow, or similar).
- 3–5+ years of experience in data engineering or applied ML.
- Strong 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 startup environment.
- MLOps tools (MLflow, BentoML, Ray Serve).
- Experience with bandit algorithms, A/B testing, or uplift modeling.
- Prior work with customer engagement, CRM, or subscription businesses.
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