- Location
- Poland · published · Romania · Serbia · Slovenia · Lithuania · Latvia
- Department
- Engineering
- Seniority
- Senior
- Source
- Workable
Description
Intetics Inc., a global technology company providing custom software application development, distributed professional teams, software product quality assessment, and “all-things-digital” solutions, is seeking a highly skilled and experienced Staff Data Scientist / Machine Learning Engineer to join our dynamic team on a full-time basis.
Responsibilities:
- Design, build, and maintain predictive Lifetime Value (pLTV) models using machine learning techniques such as XGBoost, CatBoost, and probabilistic forecasting methods.
- Develop and manage a tiered scoring framework that assigns users to different prediction models based on data availability and signal quality.
- Create and optimize calibration frameworks that transform raw model outputs into business-ready values through normalization, scaling, and custom loss optimization strategies.
- Own feature engineering processes, building and maintaining scalable feature datasets from transactional, behavioral, and engagement data.
- Define and enforce feature quality standards, ensuring model inputs are reliable, relevant, and suitable for production use. - Monitor model performance, drift, and data distribution changes, proactively identifying when retraining or model adjustments are required.
- Analyze the impact of deployed models on downstream outcomes and detect feedback loops that may affect future model accuracy.
- Develop and improve identity resolution processes to unify behavioral and transactional data across multiple data sources.
- Build, test, and maintain production-grade Python services and data pipelines for model training, scoring, and signal delivery.
- Implement robust software engineering practices, including automated testing, CI/CD, monitoring, idempotent processing, and deployment safeguards.
- Collaborate with data, engineering, and product stakeholders to improve data quality, model effectiveness, and platform scalability.
- Operate and optimize ML workflows in cloud environments using orchestration, scheduling, and distributed processing tools.
- Ensure the reliability, accuracy, and scalability of machine learning systems that support critical business decision-making and performance optimization.