Senior Data Scientist — Manufacturing Operations

DataJobs; Austin (TX); On-site; USD 124,000 - 138,000; Full time

Job summary

Experis in Austin, TX seeks a Senior Data Scientist for an onsite contract role to turn industrial data into early warnings, predictive models, and real-time monitoring.

You will analyze manufacturing datasets, build models for process monitoring, quality and equipment health, and collaborate with manufacturing and data engineering teams to deliver actionable insights.

Qualifications

• 8-10+ years of applied experience in data science, analytics, and modeling within operational or industrial environments.
• Proficiency in Python and SQL with experience handling large, complex datasets.
• Strong background in building and defending models such as classification, regression, clustering, anomaly detection, or time series analysis.
• Experience creating features from sensor, machine, quality, or operational data in manufacturing or related sectors.
• Ability to quickly learn unfamiliar plant processes and communicate technical insights effectively.

Responsibilities

• Analyze factory and industrial data to identify process anomalies and predict potential issues before they occur.
• Build and validate statistical and machine learning models for process monitoring, quality prediction, and equipment health assessment.
• Collaborate with manufacturing, quality, maintenance, and data engineering teams to develop actionable insights.
• Clean, explore, and join fragmented operational datasets, transforming signals into meaningful features.
• Communicate findings clearly to stakeholders, including explanations, thresholds, and recommended actions based on model outputs.

Skills

Python

SQL

Statistical modeling

Time series analysis

Anomaly detection

Feature engineering

Job description

Experis is hiring a Senior Data Scientist to support manufacturing operations in an onsite role in Austin, TX. This contract position focuses on turning industrial and operational data into early warnings, predictive models, and monitoring capabilities that teams can act on in real time.

In this role, you will analyze manufacturing and industrial datasets to detect anomalies, predict potential issues ahead of time, and develop models that support process monitoring, quality outcomes, and equipment health. You will work closely with manufacturing and data engineering partners to build workflows that transform raw, fragmented signals into features and insights for stakeholders.

What you’ll do

• Analyze factory and industrial data to identify process anomalies and predict potential issues before they occur.
• Build and validate statistical and machine learning models for process monitoring, quality prediction, and equipment health assessment.
• Collaborate with manufacturing, quality, maintenance, and data engineering teams to develop actionable insights.
• Clean, explore, and join fragmented operational datasets, transforming messy signals into meaningful features.
• Communicate findings clearly to stakeholders, including explanations, thresholds, and recommended actions based on model outputs.

What you bring

• 8-10+ years of applied experience in data science, analysis, and modeling within operational or industrial environments.
• Proficiency in Python and SQL, with demonstrated experience working with large, complex datasets.
• Strong background in building and defending models such as classification, regression, clustering, anomaly detection, or time series analysis.
• Experience creating features from sensor, machine, quality, or operational data, especially in manufacturing or similar sectors.
• Ability to quickly learn unfamiliar plant processes and communicate technical insights effectively.

Technology

• Python
• SQL

Contract details and pay

• Location: Austin, TX (onsite)
• Pay range: USD 90 - 100 per hour

Benefits

• Opportunity to help build a new AI-driven system from the ground up in a dynamic manufacturing environment.
• Work on cutting-edge data modeling and analytics techniques applied to real industrial challenges.
• Collaborative environment with dedicated professionals across manufacturing and data science.
• Potential for contract extension beyond an initial 5 months based on project needs and performance.
• Chance to make a tangible impact on manufacturing processes and operational efficiency.