# 1199 | Staff Data Scientist / Machine Learning Engineer

**Company:** [Intetics](https://jobs.workable.com/companies/gDvZSRWtedxcHZawvZWQAz.md)
**Location:** Remote
**Workplace:** remote

[Apply for this job](https://jobs.workable.com/view/007651a0-d563-439e-9074-9cbd375a2bb9)

## 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.

## 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).