1199 | Staff Data Scientist / Machine Learning Engineer

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. Requirements 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).