Senior Data Scientist / ML Engineer
Summary
Build and deploy ML models for a marketing analytics platform, covering revenue forecasting, audience segmentation, and anomaly detection. Work with Python, AWS, dbt, Snowflake, and Airflow in a senior data science/ML engineering role.
Design, build, and deploy ML models into production for a marketing analytics platform.
Responsibilities:
- Ship ML models to production: problem framing through monitoring and iteration
- Build predictive features (revenue forecasting, probabilistic attribution, audience segmentation, anomaly detection)
- Collaborate with Product/Engineering on how ML is surfaced to customers
- Build/maintain scalable ML infrastructure and pipelines with data engineering
- Establish best practices for model evaluation, versioning, monitoring
Requirements:
- 4+ years in data science or ML engineering with production track record
- Python and the core ML stack (gradient boosting, neural nets)
- Solid MLOps understanding (post-deployment behavior, not just notebooks)
- Cloud (AWS) and modern data infra (dbt, Snowflake, Airflow or similar)
- Strong statistical foundations; clear communicator; genuine AI-tool use
- English B1