14 days ago
Remote, Poland +5 moreStaff+
Responsibilities
- Design, build, maintain, and optimize predictive Lifetime Value models using XGBoost, CatBoost, and probabilistic forecasting methods.
- Develop tiered scoring and calibration frameworks that convert model outputs into business-ready values.
- Own feature engineering, scalable feature datasets, feature quality standards, and data validation processes.
- Monitor model performance, drift, data distributions, downstream outcomes, and feedback loops, initiating retraining or model adjustments when needed.
- Develop identity resolution processes for unifying behavioral and transactional data across sources.
- Build, test, and maintain production Python services and data pipelines for model training, scoring, and signal delivery.
- Implement automated testing, CI/CD, monitoring, idempotent processing, and deployment safeguards.
- Operate and optimize machine learning workflows in cloud environments using orchestration, scheduling, and distributed processing tools.
- Collaborate with data, engineering, and product stakeholders to improve data quality, model effectiveness, platform scalability, and system reliability.
- Integrate model outputs with external systems such as advertising, recommendation, pricing, and bidding platforms.
Requirements
- At least 5 years of hands-on ML engineering experience in Python with production-deployed models and production-grade code.
- Production experience with XGBoost, CatBoost, or LightGBM, including hyperparameter tuning, feature-importance analysis, and model evaluation beyond default metrics.
- Strong SQL and BigQuery fluency for feature engineering queries, scheduled transformations, and data validation logic.
- Production experience with GCP, including Cloud Run, Airflow/Composer, Vertex AI, and Pub/Sub.
- Understanding or experience with model calibration and the effects of prediction errors on downstream optimization systems.
- Experience integrating model outputs into external systems through outbound APIs.
- Preferred: e-commerce or DTC experience involving customer lifetime value, purchase prediction, churn modeling, or customer segmentation.
- Preferred: familiarity with BG/NBD, Gamma-Gamma, or similar probabilistic customer models, or strong Bayesian statistics foundations.
- Preferred: experience with Google Ads API, ConversionUploadService, Enhanced Conversions, Meta Conversions API (CAPI), or similar server-side event systems.
- Preferred: identity resolution, entity matching, graph-based identity stitching, probabilistic matching, or CDP-style cross-device identity work.
- Nice to have: Terraform, CI/CD pipeline design, Docker, causal inference, A/B testing in ad-spend environments, multi-armed bandits, Claude Code, Cursor, or similar AI-assisted coding tools.
Benefits
- Full-time position.
- Remote work from Poland.
Tech Stack
Categories
About Intetics
Intetics provides custom software development, quality assurance, and distributed engineering teams for enterprises and tech-driven companies. Its services span application and mobile development, systems integration, data processing, and GIS solutions, delivered via project work or dedicated offshore teams. Founded in 1995 and headquartered in Naples, Florida, the privately held firm holds ISO 9001 and ISO 27001 certifications.