Job Description
The role involves building and deploying manufacturing intelligence systems that analyze plant data, develop predictive models, and create operational tools to improve manufacturing efficiency, quality, and reliability in a battery materials production environment.
Key Responsibilities
- Build in-house manufacturing systems to ingest plant telemetry, live data, event logs, quality data, and operational context to improve prediction and response.
- Reconstruct equipment and process behavior from raw data to identify deviations and surface meaningful insights.
- Develop systems to detect process drift, classify fault patterns, and quantify operational risks before failures or quality issues occur.
- Transform raw manufacturing signals into reliable services and applications that enhance uptime, yield, and operational speed.
- Build and deploy machine learning models for anomaly detection, fault classification, process monitoring, and quality prediction.
- Create models linking process conditions, equipment behavior, and intermediate results to product quality and performance outcomes.
- Design and deploy production-grade APIs, model services, pipelines, and tools for day-to-day plant operations.
- Collaborate with manufacturing and engineering teams to ensure outputs are technically sound and actionable.
- Define architecture and roadmap for operations intelligence across manufacturing workflows.
Requirements
- Bachelor's, Master's, or PhD in Engineering, Computer Science, Operations Research, Industrial Engineering, or a related technical field
- Strong programming skills in Python and experience building production-quality software, internal applications, or data products beyond notebooks and dashboards
- Strong experience with scientific computing and machine learning libraries such as pandas, NumPy, SciPy, scikit-learn, statsmodels, PyTorch, TensorFlow, XGBoost, or equivalent tools
- Experience building and deploying software services, APIs, data pipelines, or internal platforms using tools such as FastAPI, Flask, SQL, Spark, Airflow, dbt, or similar technologies
- Experience working with time-series, sensor, event, equipment, MES, historian, quality, or other industrial data
- Experience training, validating, and deploying custom models for prediction, classification, anomaly detection, forecasting, optimization, or control-related use cases
- Strong systems thinking and the ability to translate ambiguous plant problems into robust technical solutions
- Experience taking technical systems from concept to deployment with measurable real-world impact
- Strong written and verbal communication skills and the ability to work effectively across technical and operational teams
- Ability to build and deploy machine learning models for anomaly detection, fault classification, process monitoring, quality prediction, forecasting, and related manufacturing use cases
- Ability to develop models that connect recipe conditions, process parameters, equipment behavior, and intermediate process results to downstream product quality and performance outcomes
- Ability to build feedforward and feedback models that use upstream signals, in-process data, and downstream results to improve decisions during execution
- Experience designing and deploying production-grade APIs, model services, pipelines, and internal tools that are reliable enough for day-to-day plant use
- Ability to partner closely with Manufacturing, Process Engineering, Controls, Quality, Data Systems, and Software teams to ensure outputs are technically sound and tied to real plant actions
Benefits & Perks
Compensation/salary range: $151,000 to $177,500
Work schedule: Onsite
Work environment perks: Inclusive and diverse environment, equal opportunity employer
Additional benefits: Benefits, perks, equity, and a competitive Total Rewards package
Ready to Apply?
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