Staff Data Scientist / Machine Learning Engineer

About the role

Intetics Inc., a global technology company offering custom software development, distributed professional teams, software quality assessment and digital solutions, is hiring a Staff Data Scientist / Machine Learning Engineer on a full-time basis. The role centers on building and operating predictive Lifetime Value (pLTV) models and the surrounding data infrastructure.

Key responsibilities

• Design, build and maintain predictive pLTV models using XGBoost, CatBoost and probabilistic forecasting methods.
• Develop and manage a tiered scoring framework that routes users to the right model based on data availability and signal quality.
• Create and optimize calibration frameworks that turn raw model outputs into business-ready values.
• Own feature engineering, building scalable feature sets from transactional, behavioral and engagement data and enforcing feature quality standards.
• Monitor model performance, drift and data distribution changes, and analyze the impact of deployed models on downstream outcomes.
• Improve identity resolution to unify behavioral and transactional data across sources.
• Build, test and maintain production-grade Python services and data pipelines, with automated testing, CI/CD, monitoring and idempotent processing.
• Collaborate with data, engineering and product stakeholders and run ML workflows in the cloud.

Required profile

• 5+ years of hands-on ML engineering in Python with production-deployed models.
• Experience with gradient boosted trees (XGBoost, CatBoost or LightGBM) in production, including hyperparameter tuning and feature importance.
• Strong SQL and BigQuery fluency; GCP production experience (Cloud Run, Airflow/Composer, Vertex AI, Pub/Sub).
• Understanding of model calibration and outbound API integrations with external systems.
• Nice to have: e-commerce or DTC experience, probabilistic customer models (BG/NBD, Gamma-Gamma), ad platform signal engineering (Google Ads, Meta CAPI), identity resolution, Terraform, Docker, causal inference and A/B testing.