Senior Data Architect / Data Scientist — Manufacturing Operations

Cbase Inc.

Austin (TX)

On-site

USD 165,000 - 248,000

Full time

8 hours ago

Be an early applicant

Application generator

Turn this role into an interview — a resume and cover letter built around what this employer wants.

Get past ATS filters

Job summary

Cbase Inc. in Austin, TX seeks a Senior Data Architect / Data Scientist to work with manufacturing operations. You will analyze factory data, build robust models, and work with operations to turn insights into action.

The role focuses on data understanding, feature creation, and model validation, partnering with manufacturing, quality, maintenance, data engineering, and software teams. It is not a data platform owner role.

Qualifications

Bachelor’s or master’s in a quantitative or engineering field.
10+ years of applied data science on real operational datasets.
Strong Python and SQL with experience handling large, messy tables.
Production-ready models with clear validation and explainability.
Experience with industrial/manufacturing data preferred.

Responsibilities

Frame manufacturing problems with engineering partners and validate ground truth.
Explore, clean, and join fragmented operational data (machines, sensors, quality, maintenance).
Build and validate statistical and ML models for anomaly, quality, health, and process monitoring.
Deliver usable outputs to engineers with thresholds and explanations.
Collaborate with data engineering and software on pipelines and production in Databricks.

Skills

Python

SQL

Data analysis

Modeling

Feature engineering

Communication

Learning quickly

Education

Bachelor's or Master's in quantitative field

Tools

Databricks

Spark/PySpark

MLOps

Job description

Senior Data Architect / Data Scientist — Manufacturing Operations

Work location - TX - Austin Monday - Friday 8AM to 5PM Need Only GC and USC - Contract W2

We are hiring a Senior Data Scientist to work with factory and industrial data: find what is going wrong (or about to), build models that hold up against plant reality, and help the team act on them.

Most of the job is in the data — understanding how a process behaves, cleaning noisy and incomplete signals, defining “normal” vs “abnormal” with people who run the line, creating features, validating against real outcomes, and explaining limits when the data cannot support a model. You will partner with manufacturing, quality, maintenance, data engineering, and software. You will not own the data platform. This is not a research lab role and not a platform-engineering role.

What we are hiring for

Someone who can walk a real example: this was the grain of the data, this is what I found, this is the model, this is how I knew it was wrong or right, this is what operations did with it.

Typical problems: process drift, abnormal machine behavior, quality prediction, equipment health, bottlenecks, downtime, scrap/rework, root-cause support. Methods follow the problem (statistical limits, clustering, isolation forest, time series, autoencoders, supervised models when labels exist) — we do not hire to a method list.

Manufacturing experience is a plus. We will also consider people from industrial IoT, equipment, quality, automotive, semiconductor, energy, telecom/ops, or similar operational environments who have done this loop on messy sensor or process data.

What this role is not

Data engineering / data modeling (lakehouse, medallion, star schema, Unity Catalog, ADF, “pipelines for the DS team”)
MLOps / ML platform (Airflow, SageMaker plumbing, FastAPI services, CI/CD) with little analysis of a dataset
GenAI product work (RAG chatbots, LangChain agents, Copilot apps) as the primary story
BI/reporting (Power BI/Tableau KPI apps) without model work
Resumes that list MES, SCADA, PLC, JPH, Databricks, and anomaly detection but never name a dataset, a finding, or a validation result

Those skills exist on the team or in partner teams. We need the person who works the data.

Responsibilities

Frame manufacturing problems with plant and engineering partners; push back when labels, ground truth, or “accuracy” expectations are not real.

Explore, clean, and join fragmented operational data (machines, sensors, quality, maintenance, production, MES/historian extracts — you do not need to have used every acronym).

Build and validate statistical and machine-learning models for anomaly, quality, health, and process monitoring; report false positives/negatives and business cost, not only a leaderboard metric.

Hand usable outputs to engineers and operators (thresholds, explanations, “what to do when this fires”), and support models after they are in use.

Work with data engineering and software on pipelines, Databricks, and production — you are the customer of the platform, not the person hired to build it.

Required qualifications

Bachelor’s or master’s in a quantitative or engineering field (data science, CS, statistics, industrial/mechanical/manufacturing engineering, OR, applied math, or related).

10+ years of applied data science (analysis, feature work, statistical or ML modeling on real operational or business datasets). Count data-science years, not total years in IT, DBA, or software engineering.

Strong Python and SQL; evidence of working large, messy tables — not only notebooks on clean extracts.

Production of models you can defend: classification, regression, clustering, anomaly detection, or time series, with a clear target and validation approach.

Experience creating features from machine, sensor, process, quality, maintenance, or other operational data (industrial preferred; high-volume ops data from adjacent domains is acceptable).

Comfort telling stakeholders when a model should not ship.

Ability to learn an unfamiliar plant process quickly.

Preferred qualifications

Time in manufacturing, industrial IoT, semiconductor, automotive, aerospace, energy, or equipment-heavy operations.

Databricks, Spark/PySpark, or similar cloud analytics (we use Databricks; we do not require you to have been the lakehouse owner).

Familiarity with MLOps (tracking, monitoring, drift) as a partner to platform teams.

SPC, explainability, or prior work with historians/MES data.

Business Group – manufacturing, sup group is handling manufacturing propulsions, software interface, production lines, manage product quality as well as propulsions system data

Team structure – they will not really interface with people outside of their group, mainly just working with their current team, their Product Owner interacts with the other departments as needed, the coding team size is 4-5 people, the larger team is around 20 people

Motivator for this position – they are building a brand-new application/system and need help with this process

Chance for an extension – yes at least a year

Typical Day in the Role

From scratch build the whole system up

Have access to all manufacturing data, quality data, etc

Want to build AI application

Build a data model

This person can be creative after reviewing the data, help them come up with more ideas and help their business with AI

Compelling Story & Candidate Value Proposition