Staff Data Scientist / Machine Learning Engineer
I
Intetics Inc.E-commerce technology
Workable workplace: remote; Workable locations: Poland. Romania. Serbia. Slovenia. Lithuania. LatviaFull-TimeStaff
Salary not disclosed
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Job Details
- Experience
- 5+ years of hands-on ML engineering in Python with production-deployed models
- Required Skills
- PythonSQLGCPMachine LearningBigQuery
Requirements
- Have 5+ years of hands-on ML engineering experience in Python with models deployed to production.
- Write production-grade code, not only prototypes or Jupyter notebooks.
- Have production experience with gradient-boosted trees such as XGBoost, CatBoost, or LightGBM, including hyperparameter tuning, feature importance analysis, and model evaluation beyond default metrics.
- Demonstrate strong SQL and BigQuery fluency for feature engineering queries, scheduled transformations, and data validation.
- Have production GCP experience deploying and maintaining ML workloads; relevant tools include Cloud Run, Airflow/Composer, Vertex AI, and Pub/Sub.
- Understand model calibration and how prediction errors can compound in downstream optimization systems.
- Have experience sending model outputs to external systems such as ad platforms, recommendation engines, pricing systems, or bidding systems.
- Preferred: E-commerce or DTC experience in 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, Meta Conversions API (CAPI), or similar server-side event dispatch systems.
- Preferred: Identity resolution or entity matching experience, including 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 bandit allocation, or AI-assisted coding tools such as Claude Code or Cursor.
Responsibilities
- Design, build, and maintain predictive Lifetime Value (pLTV) models using XGBoost, CatBoost, and probabilistic forecasting methods.
- Develop and manage a tiered scoring framework that assigns users to prediction models based on data availability and signal quality.
- Create and optimize calibration frameworks that transform raw model outputs into business-ready values.
- Build and maintain scalable feature datasets from transactional, behavioral, and engagement data, and enforce feature quality standards.
- Monitor model performance, drift, and data distribution changes; identify when retraining or model adjustments are required.
- Analyze deployed models’ downstream impact and detect feedback loops that may affect future accuracy.
- Develop identity resolution processes to unify behavioral and transactional data across multiple sources.
- Build, test, and maintain production-grade 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 ML workflows in cloud environments using orchestration, scheduling, and distributed processing tools.
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