Staff Data Scientist & ML Engineer — Production-Grade ML

Intetics Inc.; United States; Remote; USD 120,000 - 180,000; Full time

Job summary

Intetics Inc., a global technology company, seeks a Staff Data Scientist / Machine Learning Engineer to design, build, and optimize predictive models (pLTV) and production-grade data pipelines in a cloud environment. You will own feature engineering, calibration, and model monitoring across transactional, behavioral, and engagement data to drive business outcomes.

Responsibilities include implementing scalable ML workflows, collaborating with cross-functional teams, ensuring model quality, and

Qualifications

• 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, feature importance analysis, and model evaluation beyond default metrics.
• Strong SQL and BigQuery fluency. You will write complex feature engineering queries, scheduled transformations, and data validation logic directly in BigQuery.
• GCP production experience. Cloud Run, Airflow/Composer, Vertex AI, Pub/Sub.
• Understanding of model calibration and its impact on business value; experience with downstream optimization.
• Outbound API integration experience; pushing model outputs to external systems (ads, recommendations, pricing, bidding).
• E-commerce or DTC domain experience (LTV, purchase prediction, churn, segmentation).
• Familiarity with probabilistic models (BG/NBD, Gamma-Gamma) or strong Bayesian statistics foundations.
• Ad platform signal engineering (Google Ads API, Meta CAPI).
• Identity resolution or entity matching; graph-based stitching or CDP-style cross-device work.
• 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 tools (Claude Code, Cursor).

Responsibilities

• Design, build, and maintain predictive Lifetime Value models using ML techniques.
• Develop and manage a tiered scoring framework for model selection based on data availability.
• Create and optimize calibration frameworks transforming raw model outputs into business-ready values.
• Own feature engineering and maintain scalable feature datasets from transactional, behavioral, and engagement data.
• Define and enforce feature quality standards for production readiness.
• Monitor model performance, drift, and data distribution; trigger retraining when needed.
• Analyze impact of models on downstream outcomes and detect feedback loops.
• Develop identity resolution processes to unify data across sources.
• Build, test, and maintain production-grade Python services and data pipelines for training, scoring, and signaling.
• Implement robust software practices: automated testing, CI/CD, monitoring, idempotent processing, safeguards.
• Collaborate with data, engineering, and product stakeholders to improve data quality and platform scalability.
• Operate ML workflows in cloud environments with orchestration and distributed processing tools.
• Ensure reliability, accuracy, and scalability of ML systems supporting business decisions.

Skills

Python

Production ML

XGBoost

CatBoost

LightGBM

BigQuery

SQL

GCP

Airflow

CI/CD

Terraform

Bayesian statistics

API integration

Data pipelines

Identity resolution

Tools

Docker

Kubernetes

Cloud Run

Vertex AI

Job description

Intetics Inc., a global technology company, seeks a Staff Data Scientist / Machine Learning Engineer to design, build, and optimize predictive models (pLTV) and production-grade data pipelines in a cloud environment. You will own feature engineering, calibration, and model monitoring across transactional, behavioral, and engagement data to drive business outcomes.

Responsibilities include implementing scalable ML workflows, collaborating with cross-functional teams, ensuring model quality, and