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
Strongly Preferred:
Nice-to-have:
Drive machine learning and AI for identity and risk by building, deploying, and monitoring risk models and scalable modeling tools that help protect users, improve products, and inform risk decisions.
Develop and deploy multimodal machine learning methods that derive precise phenotypes from complex human data, characterize patient state, predict clinical outcomes, and support biomarker and therapeutic target discovery.
Leads the design and delivery of advanced machine learning models and strategic AI initiatives, mentoring data science colleagues and translating business challenges into scalable, data-driven solutions.
Apply and validate machine learning models in active drug discovery programs, translating model performance and uncertainty into actionable insights for experimental teams and guiding improvements with ML and engineering collaborators.
Own the strategy and systems that score, select, and provision large-scale driving datasets for model training, balancing data value and quality with storage costs as the corpus grows.
