Data Scientist (Full time)

Location: Grand Prairie, Texas 75050 • Full-time

About the role

We are seeking a Data Scientist to build and deploy machine learning models for predictive maintenance, anomaly detection, demand forecasting, and root cause analysis in a manufacturing environment. You will design scalable cloud data pipelines for high-volume IoT and manufacturing data, translate complex model outputs into actionable recommendations for operations and executive stakeholders, and design A/B experiments and simulations to validate process changes and quantify business impact before full deployment.

Responsibilities

• Build and deploy ML models for predictive maintenance, anomaly detection, demand forecasting, and root cause analysis
• Develop scalable cloud data pipelines for high-volume manufacturing and IoT data using Spark, Kafka, Airflow, and Delta Lake
• Translate complex model outputs into clear, actionable recommendations for operations and executive stakeholders
• Design A/B experiments and simulations to validate process changes and quantify business impact before full deployment

Requirements

• 5+ years of experience as a Data Scientist
• Strong SQL and Python proficiency with hands-on experience in medallion/Lakehouse architectures on Databricks, Snowflake, AWS, or Azure
• Proven track record building and deploying ML models for predictive maintenance, anomaly detection, demand forecasting, and root cause analysis
• Skilled in building scalable cloud data pipelines for high-volume manufacturing and IoT data using Spark, Kafka, Airflow, and Delta Lake
• Strong communicator able to translate complex model outputs into clear, actionable recommendations
• Experience designing A/B experiments and simulations to validate process changes and quantify business impact

Preferred qualifications

• 2-4 years working in the manufacturing domain
• Experience in shop floor operations, production planning, and systems including MES, SCADA, and ERP
• Proficient in industrial protocols (OPC-UA, MQTT, Modbus) with ability to bridge OT/IT systems for real-time data extraction
• Applied experience with OEE, Six Sigma, SPC, and lean methodologies to drive measurable gains in yield, uptime, and efficiency
• Proficient in scikit-learn, TensorFlow, or PyTorch with experience moving models from prototype to production in industrial environments
• Solid grounding in statistical methods — time series, regression, clustering, and hypothesis testing applied to manufacturing quality problems