1199 | Data Scientist / Machine Learning Engineer

Intetics; 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 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.

Задачи:

• 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.

Требования:

• 5+ years of hands-on ML engineering in Python with production-deployed models (not just Jupyter notebooks). You should be comfortable writing production-grade code, not just prototypes.
• 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. 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. You have deployed and maintained ML workloads on GCP, not just completed certification exercises.
• Understanding of model calibration. You know the difference between a model that minimizes RMSE and a model that maximizes business value. You have experience with or strong intuition about how prediction errors compound when consumed by a downstream optimization system.
• Outbound API integration experience. You have pushed model outputs into external systems that act on them (ad platforms, recommendation engines, pricing systems, bidding systems).
• 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). If you haven't used these specifically, strong Bayesian statistics foundations are equally valuable.
• Ad platform signal engineering. Experience with Google Ads API (ConversionUploadService, Enhanced Conversions), Meta Conversions API (CAPI), or similar server-side event dispatch systems.
• Identity resolution or entity matching. Graph-based identity stitching, probabilistic matching, or CDP-style cross-device identity 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 workflows (Claude Code, Cursor, or similar AI-assisted coding tools).