[Hiring] Staff Data Scientist / Machine Learning Engineer @Intetics

Role 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.

• 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.

Qualifications

• 5+ years of hands-on ML engineering in Python with production-deployed models (not just Jupyter notebooks).
• Experience with gradient boosted trees (XGBoost, CatBoost, or LightGBM) in a production setting, including hyperparameter tuning, feature importance analysis, and model evaluation beyond default metrics.
• Strong SQL and BigQuery fluency.
• GCP production experience (Cloud Run, Airflow/Composer, Vertex AI, Pub/Sub).
• Understanding of model calibration.
• Outbound API integration experience.

Requirements

• Strongly Preferred:
• E-commerce or DTC domain experience (customer lifetime value, purchase prediction, churn modeling, or customer segmentation for retail or subscription businesses).
• Familiarity with probabilistic customer models (BG/NBD, Gamma-Gamma, or similar frameworks).
• Ad platform signal engineering experience (Google Ads API, Meta Conversions API, or similar server-side event dispatch systems).
• Identity resolution or entity matching experience (graph-based identity stitching, probabilistic matching, or CDP-style cross-device identity work).
• Nice-to-have:
• Terraform, CI/CD pipeline design, Docker containerization for ML workloads.
• Experience with causal inference methods, A/B testing in ad spend environments, or multi-armed bandit allocation.
• Familiarity with agentic development workflows (Claude Code, Cursor, or similar AI-assisted coding tools).