Machine Learning Operations Engineer - Database Marketing

Job Description Roles & Responsibilities The primary duties and responsibilities of this role are: Deploy, monitor, and maintain machine learning models used across gaming marketing and player analytics. Operationalize Data Science outputs by building automated pipelines for scoring, segmentation, and campaign ready datasets. Ensure high reliability and uptime for model workflows, including job scheduling, error detection, and alerting. Collaborate with Data Scientists to productionize new models, optimize runtime performance, and improve stability. Build and maintain data pipelines supporting model features, input refreshes, and scheduled scoring processes. Implement QA processes to identify data drift, model degradation, and operational anomalies. Maintain version control and documentation for models, pipelines, and deployment procedures. Partner with Campaign Operations to ensure model outputs align with business logic, targeting requirements, and production schedules. Work with IT and infrastructure teams on compute resources, storage, and system performance. Support model retraining cycles, testing, and controlled rollouts of updated logic. Ensure compliance with internal data governance and security standards. About You: Master s or PhD in Computer Science, Engineering, Data Science, Statistics or a related technical field required. 5+ years of experience in Data Science, ML Ops, data engineering, or production level machine learning environments. Strong experience with Python, SQL, and workflow orchestration tools (Airflow or similar). Experience working with cloud-based data platforms. Familiarity with machine learning model lifecycle management, CI/CD pipelines, and version control (Git). Able to translate analytical requirements into stable, production ready workflows. Comfortable working in fast paced environments with daily and weekly operational deliverables. Desired Candidate Profile Master s or PhD in Computer Science, Engineering, Data Science, Statistics or a related technical field required. 5+ years of experience in Data Science, ML Ops, data engineering, or production level machine learning environments. Strong experience with Python, SQL, and workflow orchestration tools (Airflow or similar). Experience working with cloud-based data platforms. Familiarity with machine learning model lifecycle management, CI/CD pipelines, and version control (Git). Able to translate analytical requirements into stable, production ready workflows. Comfortable working in fast paced environments with daily and weekly operational deliverables. Company Industry HotelsHospitality Department / Functional Area IT Software Keywords Machine Learning Operations Engineer - Database Marketing Get real-time job updates only on our App