Senior Data Scientist, AI for Manufacturing Ops Onsite

EPITEC; Austin (TX); On-site; USD 134,000 - 147,000; Full time

Job summary

EPITEC is seeking a Senior Data Scientist to dig into manufacturing and quality data, build models, and help apply AI to operations. You will work on data exploration, feature creation, and validating models that support plant performance and process monitoring.

You will collaborate with engineers, operators, and data teams to deliver actionable outputs, thresholds, and explanations while focusing on real-world data challenges and rapid impact.

Qualifications

• 8–10+ years of applied data science experience on real operational datasets.
• Strong Python and SQL skills with large, messy tables.
• Experience building defensible models (classification, regression, anomaly detection, time series) with validation.
• Ability to derive meaningful insights from incomplete datasets.

Responsibilities

• Frame manufacturing problems with plant and engineering partners.
• Explore, clean, and join fragmented operational data (machines, sensors, quality, maintenance, MES/historian).
• Build and validate ML models for anomaly, quality, health, and process monitoring.
• Explain how a model is validated and when it should ship.
• Collaborate with data engineering on pipelines and production (Databricks).
• Provide actionable outputs to engineers and operators with thresholds and explanations.
• Identify opportunities to apply AI to manufacturing operations.

Skills

Python

SQL

Data modeling

ML modeling

Feature engineering

Stakeholder communication

Education

Bachelor's or Master’s in Data Science, CS, Engineering, or related field

Tools

Databricks

Spark/PySpark

Job description

EPITEC is seeking a Senior Data Scientist to dig into manufacturing and quality data, build models, and help apply AI to operations. You will work on data exploration, feature creation, and validating models that support plant performance and process monitoring.

You will collaborate with engineers, operators, and data teams to deliver actionable outputs, thresholds, and explanations while focusing on real-world data challenges and rapid impact.