1199 | Staff Data Scientist / Machine Learning Engineer

Intetics; United States; Remote; USD 150,000 - 190,000; Full time

Job summary

Intetics Inc., a global technology company, is seeking a highly skilled Staff Data Scientist / Machine Learning Engineer to join our team on a full-time basis in the United States. The role focuses on designing and deploying predictive models for customer lifetime value, building scalable data pipelines, and ensuring production-grade ML systems in cloud environments.

Responsibilities include feature engineering, model calibration, drift monitoring, and collaboration with data, engineering, and

Tools

XGBoost

CatBoost

LightGBM

BigQuery

Python

GCP

CI/CD

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.

Responsibilities:

• 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.

• 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).

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). 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.

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).