Senior Data Scientist

updated linkedin || PV W2
Senior Data Scientist- Manufacturing Operations
Austin, TX -Onsite
Client- General Motors
GC
Full SSN Will be requested by PV
Virtual
Required Qualifications
Bachelor s or Master s degree in Data Science, Computer Science, Statistics, Industrial Engineering, Mechanical Engineering, Manufacturing Engineering, Applied Mathematics, or a related quantitative/engineering discipline.
6+ years of applied Data Science experience involving analysis, feature engineering, statistical modeling, or machine learning on real-world datasets.
Strong experience with Python and SQL.
Demonstrated ability to work with large, messy datasets, not only clean analytical extracts.
Strong data modeling experience.
Strong data pipeline experience.
Strong data analytics experience with the ability to extract meaningful insights from complex data.
Experience building and defending production-quality models involving one or more of:
Classification
Regression
Clustering
Anomaly detection
Time-series modeling
Experience creating features from operational, machine, sensor, process, quality, or maintenance data.
Ability to explain analytical findings and model limitations to technical and business stakeholders.
Ability to quickly understand unfamiliar manufacturing or operational processes.
Preferred Qualifications
Manufacturing, automotive, industrial IoT, semiconductor, aerospace, energy, or equipment-heavy industry experience.
Experience with Databricks, Spark, or PySpark.
Experience working with MES or historian data.
Knowledge of Statistical Process Control (SPC).
Experience with model explainability.
Familiarity with MLOps concepts such as model tracking, monitoring, and drift.
Experience working with high-volume operational or industrial data.
Important: What This Role Is NOT
Candidates whose recent experience is primarily focused on the following may not be aligned:
Data engineering / lakehouse architecture
Medallion architecture / star schema / Unity Catalog
MLOps or ML platform engineering
Building CI/CD or infrastructure platforms
GenAI/RAG/LangChain applications as the primary experience
Power BI/Tableau reporting without substantial modeling
Building AI platforms without hands-on data analysis and modeling
We are looking for a Data Scientist who works directly with the data.
Job Summary
We are seeking a Senior Data Scientist Manufacturing Operations to work with factory and industrial data and help identify operational issues, develop data-driven models, and deliver actionable insights to manufacturing teams.
This is a hands-on Data Science role focused primarily on data analysis, modeling, and validation. The ideal candidate can work with large, messy operational datasets, identify meaningful patterns, build statistical or machine-learning models, and translate the results into practical recommendations for engineering and operations teams.
This is not primarily a data engineering, MLOps, platform engineering, or GenAI role.
Key Responsibilities
Analyze and interpret complex manufacturing and operational datasets.
Work with fragmented data from machines, sensors, quality, maintenance, production, and MES/historian systems.
Clean, transform, join, and prepare large and messy datasets for analysis and modeling.
Partner with manufacturing, quality, maintenance, engineering, data engineering, and software teams to define business problems.
Develop and validate statistical and machine-learning models for:
o Anomaly detection
o Quality prediction
o Equipment health
o Process monitoring
o Process drift
o Downtime
o Bottleneck identification
o Scrap/rework reduction
o Root-cause analysis
Perform feature engineering using machine, sensor, process, quality, and maintenance data.
Evaluate model performance using appropriate validation approaches.
Analyze false positives, false negatives, and potential business impact not just model accuracy.
Communicate model limitations and determine when a model should or should not be deployed.
Provide actionable outputs to engineers and operators, including thresholds, explanations, and recommended actions.
Collaborate with data engineering and software teams on production pipelines and Databricks environments.
Support models after deployment and help identify opportunities for additional AI/data science applications.
Review manufacturing data and creatively identify opportunities to improve quality, production, and operational performance.