1199 | Staff Data Scientist / Machine Learning Engineer Remote job

Intetics Inc.; United States; Remote; USD 160,000 - 230,000; Full time

Job summary

Intetics Inc. is seeking a Staff Data Scientist / Machine Learning Engineer to join our team in the United States. This full‑time role focuses on designing, implementing, and maintaining production ML systems that drive business value across customer analytics and engagement signals.

You will lead feature engineering, model calibration, and production pipelines using Python, XGBoost/CatBoost, and BigQuery, with a strong emphasis on reliability, scalability, and cloud infrastructure in GCP.

Qualifications

• 5+ years hands-on ML engineering in Python with production models.
• Experience with gradient boosted trees in production and hyperparameter tuning.
• Strong SQL and BigQuery fluency for feature engineering and data validation.
• GCP production experience with Cloud Run, Airflow/Composer, Vertex AI, Pub/Sub.

Responsibilities

• Design, build, and maintain pLTV models with ML 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.

Skills

Python for ML

XGBoost/CatBoost

BigQuery

GCP

Model calibration

API integration

Job description

1199 | Staff Data Scientist / Machine Learning Engineer

Job brief

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 Engineerto 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

• 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).

About Intetics

Intetics Inc. is a leading American technology company providing custom software application development, distributed professional teams creation, software product quality assessment, and “all‑things‑digital” solutions built with SMAC, RPA, AI/ML, IoT, blockchain, and GIS/UAV/LBS technologies.

Based on proprietary pioneering business models of Offshore Dedicated Team® and Remote In‑Sourcing®, an advanced Technical Debt Reduction Platform (TETRA™) and measurable SLAs for software engineering, Intetics helps innovative organizations capitalize on global talent with our in‑depth engineering expertise based on our Predictive Software Engineering framework.

Intetics core strength lays in design of software products in conditions of incomplete specifications. We have extensive industry expertise in Education, Healthcare, Logistics, Life Sciences, Finance, Insurance, Communications, and custom ERP, CRM, Intelligent Automation and Geospatial solutions.

Our advanced software engineering background and outstanding quality management platform, along with an unparalleled methodology for talent acquisition, team building and talent retention, guarantee that our clients receive exceptional results for their projects. At Intetics, our outcomes do not just meet clients expectations, they have been exceeding them for a quarter of a century. Intetics operates from multiple offices in the USA, Europe and Latin America, hiring the best talent available worldwide.

Intetics is ISO 9001 (quality) and ISO 27001 (security) certified and a Microsoft Gold, Amazon, and UiPath Silver partner. The company’s innovation and growth achievements are reflected in winning prestigious titles and awards, including Inc5000, Software 500, CRN 100, American Business, Deloitte Fast 50, European IT Excellence, Best European BPO, Stevie People’s Choice, Clutch and ACQ5 Awards, IAOP Global Outsourcing 100 and Fortune Innovative 300 lists. You can find more information at https://intetics.com