Machine Learning Engineer

GCS Recruitment; Village of Massapequa Park (NY); On-site; USD 120,000 - 180,000; Full time

Job summary

GCS Recruitment is seeking a hands-on ML Engineer to own and improve production machine learning models forecasting timelines, demand, and risk across projects. You will engineer features, optimize performance, and deploy scalable ML solutions in AWS/Azure environments.

Responsibilities include maintaining models in production, evaluating forecasts with metrics, and collaborating with data teams to deliver actionable insights for marketing, sales, and operations.

Qualifications

• Strong Python and SQL experience with ML exposure.
• Experience building and deploying production ML models.
• Knowledge of forecasting, time-series, and feature engineering.

Responsibilities

• Own existing ML models and improve prediction accuracy.
• Engineer new features and optimize model performance.
• Deploy scalable ML solutions and monitor in production.

Skills

Python

SQL

ML/Forecasting

Time-Series Modeling

Feature Engineering

Model Evaluation

Spark

Tools

PySpark

MLflow

Docker

Kubernetes

AWS/Azure

Job description

Role Overview

We're working with a team that uses Machine Learning and Predictive Analytics to forecast project timelines, permit approvals, resource planning, customer demand, and operational risks. These predictions help drive key business decisions across marketing, sales, and operations.

The team already has machine learning models in production and is looking for someone who can take ownership of existing models, improve prediction accuracy, engineer new features, optimize performance, and deploy scalable ML solutions.

Required Skills

• Strong Python and SQL experience
• Machine Learning and Predictive Analytics
• XGBoost, Scikit-Learn, LightGBM, or similar algorithms
• Forecasting and Time-Series Modeling
• Feature Engineering and Model Optimization
• Model Evaluation (Precision, Recall, F1, ROC-AUC, MAE, RMSE)
• PySpark / Apache Spark
• MLflow, Docker, Kubernetes
• AWS or Azure Cloud
• Experience deploying and maintaining production ML models

Preferred Background

• Demand Forecasting
• Customer Churn Prediction
• Revenue Forecasting
• Risk Scoring
• Resource Planning
• Pricing Optimization
• Capacity Planning
• Supply Chain Analytics
• Operational Forecasting