Ai/ml Manufacturing Data Scientist

Talent XM is seeking an experienced AI/ML Manufacturing Data Scientist to leverage machine learning and advanced analytics to solve complex manufacturing challenges.

This role focuses on using data to optimize production processes, predict equipment failures, improve product quality, and drive efficiencies across manufacturing operations.

You will work with large-scale operational datasets, such as production, process, and sensor data, to develop predictive models, identify trends, and generate insights that significantly impact business outcomes.

The ideal candidate combines strong data science capabilities with practical manufacturing knowledge and the ability to translate analytical outputs into actionable business strategies that enhance manufacturing performance.

Key Responsibilities Data Analysis &

• Model Development: Analyze large datasets from manufacturing systems (e.g., production records, sensor data, process parameters, quality data) to uncover patterns, trends, and anomalies that influence manufacturing performance.

Develop and deploy machine learning models to predict equipment failure, optimize throughput, and improve product quality.

Predictive Maintenance &

• Process Optimization: Design predictive models for equipment failure, maintenance needs, and process anomalies using techniques like time-series analysis, regression, and classification.

Implement optimization algorithms to enhance production scheduling, resource allocation, and process parameter tuning.

Operational Decision Support: Develop real-time decision-support tools and dashboards for plant operators, engineers, and leadership that integrate machine learning insights into operational workflows, helping to reduce costs, prevent downtime, and optimize production.

Collaboration Across Teams: Work closely with cross-functional teams—such as process engineers, production managers, and maintenance personnel—to ensure that AI-driven insights align with operational realities, constraints, and business goals.

Data Integration &

• Pipeline Development: Build and maintain data pipelines to integrate data from MES, SCADA, Io T devices, and external sources.

Prepare the data for modeling, feature engineering, and analysis, ensuring high-quality, consistent datasets for use in predictive and optimization models.

Real-Time Monitoring &

• Alerts: Implement machine learning systems for real-time monitoring of production data to detect anomalies and generate alerts for potential issues, enabling proactive interventions to prevent unplanned downtime or quality defects.

Continuous Improvement: Identify opportunities for continuous improvement within manufacturing operations by applying AI/ML methods to enhance productivity, reduce waste, and improve quality across the production line.

Required Qualifications Experience: Minimum of 6-7 years of experience in data science, machine learning, or predictive analytics, with a focus on manufacturing, industrial operations, or production environments.

Proven experience in developing and deploying machine learning models to optimize manufacturing processes and improve efficiency.

Technical Skills: Strong proficiency in Python, SQL, and machine learning libraries such as scikit-learn, Tensor Flow, and Py Torch.

Hands-on experience with statistical analysis, data cleaning, and feature engineering techniques.

Familiarity with predictive maintenance models, time-series forecasting, and anomaly detection techniques applied to operational datasets.

Proficiency in optimization algorithms and methods, such as linear programming, genetic algorithms, or operations research.

Manufacturing Knowledge: Strong understanding of manufacturing concepts including production processes, quality management, equipment reliability, and process optimization.

Experience working with manufacturing systems like MES, SCADA, Io T platforms, and process control systems.

Data Engineering: Experience building data pipelines to process, clean, and integrate data from diverse sources (e.g., sensors, production systems, databases).

Communication Skills: Strong ability to communicate complex analytical results and technical findings to non-technical stakeholders, including plant operators, engineers, and senior management.

Ability to translate data insights into actionable business recommendations that can directly improve manufacturing operations.

Preferred Qualifications Advanced Tools &

• Techniques: Experience with advanced machine learning techniques, including deep learning, reinforcement learning, and natural language processing (NLP), applied to manufacturing use cases.

Cloud &

• Big Data Technologies: Familiarity with cloud platforms (AWS,