Staff Data Scientist / Machine Learning Engineer

🔥 0 minutes ago

🌐 Poland, Romania, +4 more countries – Remote

🇵🇱 Poland – Remote 🇷🇴 Romania – Remote 🇷🇸 Serbia – Remote 🇸🇮 Slovenia – Remote 🇱🇹 Lithuania – Remote 🇱🇻 Latvia – Remote

⏰ Full Time

🔴 Lead

📊 Data Scientist

👻 Ghost score 11%

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Intetics

WebsiteLinkedInAll Job Openings

501 - 1000 employees

Founded 1995

💼 Consulting

🏥 Healthcare

📦 Logistics

Consulting • Healthcare • Logistics

Intetics is an innovative company providing custom software development services, specializing in AI and machine learning solutions. They offer Remote In-Sourcing® to build expert teams for software engineering and data processing projects, along with advanced tools like TETRA™ for software quality assessment. Intetics aims to empower businesses by leveraging high-quality data and integrating modern technologies across various industries, including healthcare, finance, and more.

📋 Description

• Design, build, and maintain predictive Lifetime Value (pLTV) models using XGBoost, CatBoost, and probabilistic forecasting methods • Develop and manage a tiered scoring framework assigning users to prediction models based on data availability and signal quality • Create and optimize calibration frameworks that convert raw model outputs into business-ready values • Own feature engineering processes and maintain scalable feature datasets from transactional, behavioral, and engagement data • Define and enforce feature quality standards for reliable production model inputs • Monitor model performance, drift, and data distribution changes; identify retraining or model adjustment needs • Analyze deployed-model impact on downstream outcomes and detect feedback loops affecting model accuracy • Improve identity resolution across behavioral and transactional data 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 • Collaborate with data, engineering, and product stakeholders on data quality, model effectiveness, and platform scalability • Operate and optimize ML workflows in cloud environments using orchestration, scheduling, and distributed processing tools • Ensure reliability, accuracy, and scalability of ML systems supporting business decision-making and performance optimization

🎯 Requirements

• 5+ years of hands-on ML engineering in Python with production-deployed models • Production-grade Python coding experience beyond prototypes or Jupyter notebooks • Production experience with gradient boosted trees including XGBoost, CatBoost, or LightGBM, hyperparameter tuning, feature importance analysis, and model evaluation beyond default metrics • Strong SQL and BigQuery fluency, including complex feature engineering queries, scheduled transformations, and data validation logic • Production experience with GCP, including Cloud Run, Airflow/Composer, Vertex AI, and Pub/Sub • Understanding of model calibration and the relationship between prediction errors and downstream optimization systems • Experience integrating outbound APIs to deliver model outputs to external systems • E-commerce or DTC domain experience preferred • Familiarity with probabilistic customer models such as BG/NBD or Gamma-Gamma preferred • Ad platform signal engineering experience preferred, including Google Ads API or Meta Conversions API • Identity resolution or entity matching experience preferred • Terraform, CI/CD pipeline design, and Docker containerization experience nice-to-have • Experience with causal inference, A/B testing in ad spend environments, or multi-armed bandit allocation nice-to-have • Familiarity with agentic development workflows such as Claude Code or Cursor nice-to-have

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