Staff Data Scientist (Manufacturing)

TL;DR

Staff Data Scientist (Manufacturing): Building and deploying machine learning models for predictive maintenance and anomaly detection in industrial environments with an accent on OT/IT data integration and cloud-based data pipelines. Focus on bridging shop floor operations with advanced analytics to drive measurable improvements in production efficiency and quality.

Location: Must be based in the United States

Company

A global Digital Product Engineering company with over 17,000 experts across 39 countries, focused on building innovative digital products and experiences.

What you will do

• Design and deploy ML models for predictive maintenance, anomaly detection, and demand forecasting.
• Build scalable cloud data pipelines for high-volume manufacturing and IoT data using Spark, Kafka, and Airflow.
• Bridge OT and IT systems to enable real-time data extraction from industrial protocols like OPC-UA and MQTT.
• Translate complex model outputs into actionable recommendations for operations and executive stakeholders.
• Apply statistical methods and lean methodologies to optimize production planning and shop floor efficiency.
• Design A/B experiments and simulations to validate process changes before full-scale deployment.

Requirements

• Must be based in the United States
• Minimum 6 years of experience as a Data Scientist.
• Strong proficiency in SQL and Python with experience in medallion/lakehouse architectures on Databricks, Snowflake, AWS, or Azure.
• Proven track record of moving ML models from prototype to production in industrial environments.
• Experience with industrial systems including MES, SCADA, and ERP.
• Solid grounding in statistical methods such as time series, regression, and hypothesis testing.

Nice to have

• 8–10 years of experience in manufacturing environments.
• Hands-on experience with OEE, Six Sigma, SPC, and lean methodologies.
• Proficiency in deep learning frameworks like TensorFlow or PyTorch.

Culture & Benefits

• Dynamic and non-hierarchical work culture.
• Opportunity to work on large-scale digital products across diverse industries.
• Global environment with a focus on diversity and inclusion.
• Commitment to equal employment opportunities for all applicants.