Forecasting ML Engineer — Production Models & Analytics

GCS Recruitment; Kennewick (WA); On-site; USD 120,000 - 160,000; Full time

Job summary

GCS Recruitment is seeking a proficient Machine Learning Engineer to own and enhance predictive models used for forecasting timelines, demand, and risks. You will engineer features, optimize models, and deploy scalable solutions in a production environment.

The role emphasizes strong Python/SQL skills, experience with Spark, MLflow, Docker, and cloud platforms (AWS or Azure). Collaboration with teams across marketing, sales, and operations is expected.

Qualifications

• Strong Python and SQL experience.
• Experience with ML/Predictive Analytics in production environments.
• Familiarity with XGBoost, Scikit-Learn, LightGBM, or similar algorithms.
• Forecasting and time-series modeling.
• Feature engineering and model optimization.

Responsibilities

• Take ownership of existing ML models and improve prediction accuracy.
• Develop new features and optimize model performance for production.
• Deploy scalable ML solutions and monitor them in cloud environments.
• Collaborate with cross-functional teams across marketing, sales, and operations.
• Evaluate models using metrics like precision, recall, F1, ROC-AUC, MAE, RMSE.

Skills

Python

SQL

Machine Learning

Predictive Analytics

XGBoost

Scikit-Learn

LightGBM

Forecasting

Time-Series Modeling

Feature Engineering

Model Optimization

Model Evaluation

PySpark

Apache Spark

MLflow

Docker

Kubernetes

AWS

Azure Cloud

Production ML Deployment

Tools

MLflow

Docker

Kubernetes

Apache Spark

PySpark

AWS

Azure

Job description

GCS Recruitment is seeking a proficient Machine Learning Engineer to own and enhance predictive models used for forecasting timelines, demand, and risks. You will engineer features, optimize models, and deploy scalable solutions in a production environment.

The role emphasizes strong Python/SQL skills, experience with Spark, MLflow, Docker, and cloud platforms (AWS or Azure). Collaboration with teams across marketing, sales, and operations is expected.