Description

Key Responsibilities
Applied Machine Learning & Analytics

• Develop machine learning and statistical models to support manufacturing use cases such as anomaly detection, quality prediction, equipment health, process monitoring, throughput improvement, and decision support.
• Apply supervised, unsupervised, and semi-supervised learning methods, including classification, regression, clustering, anomaly detection, time-series analysis, statistical process control, and model explainability.
• Build anomaly detection solutions using methods such as control limits, isolation forests, clustering, Mahalanobis distance, autoencoders, time-series models, and supervised classification where labeled defects are available.
• Develop models for manufacturing use cases such as assembly issues, predictive maintenance, bottleneck detection, process optimization, and quality prediction.
• Evaluate model performance using appropriate metrics, ground truth definitions, validation strategies, false positive and false negative analysis, and business impact measures.
• Identify when data is insufficient, labels are unreliable, ground truth is weak, or a machine learning approach is not appropriate, and communicate those limitations clearly.

Manufacturing Data & Feature Engineering

• Analyze real-time and historical factory data from sources such as PLCs, sensors, machines, MES, SCADA, historians, quality systems, maintenance systems, production logs, and enterprise platforms.
• Create features from manufacturing signals such as cycle time, pressure, temperature, torque, vibration, current, force, cushion pressure, line speed, JPH, FTT, FRC, scrap, rework, downtime, and fault codes.
• Work with noisy, incomplete, high-frequency, or fragmented industrial data to create reliable analytical datasets.
• Partner with plant teams and domain experts to understand process behavior, validate assumptions, and determine whether model outputs reflect real operating conditions.

Cloud, Data Pipelines & MLOps

• Use cloud data platforms, preferably Databricks, to support scalable analytics and machine learning workflows.
• Develop and partner with Data Engineering to build data pipelines that ingest, transform, and prepare manufacturing data for analysis, modeling, monitoring, and reporting.
• Work with tools such as Cloud Storage, databricks, bigdata processing (pyspark, spark) 
• Support real-time and near-real-time analytics use cases by working with streaming data or event-driven architectures.
• Partner with platform and software engineering teams to move models and analytical workflows from prototype to production-ready solutions.
• Follow MLOps practices such as experiment tracking, model versioning, model deployment, model monitoring, drift detection, retraining workflows, and production documentation.
• Monitor model performance after deployment, including false positives, false negatives, data drift, model drift, latency, uptime, pipeline failures, and changing manufacturing conditions.

Productization, Communication & Delivery

• Collaborate with data engineers, platform engineers, software engineers, manufacturing engineers, quality teams, and plant stakeholders to move data science prototypes into production-ready workflows.
• Follow software engineering best practices, including version control, modular code, code reviews, testing, logging, documentation, reusable packages, and reproducible environments.
• Document model logic, assumptions, input features, thresholds, limitations, operational dependencies, and recommended actions for business and plant-floor users.
• Distinguish between exploratory research, prototype development, and production-ready delivery, with focus on prototype development and production-ready delivery

Required Qualifications

• Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Industrial Engineering, Mechanical Engineering, Manufacturing Engineering, Operations Research, Applied Mathematics, or a related technical field.
• 6+ years of experience applying data science, machine learning, statistical modeling, optimization, or advanced analytics in a professional environment.
• Strong Python skills.
• Strong SQL skills and experience working with large, complex datasets.
• Experience with supervised and unsupervised machine learning methods, including classification, regression, clustering, anomaly detection, time-series analysis, forecasting, or process optimization.
• Experience building features from machine, sensor, process, quality, maintenance, production, or operational datasets.
• Experience working with cloud-based data and analytics platforms such as databricks.
• Experience working with data engineering, software engineering, or platform teams to move analytical solutions toward production.
• Understanding of MLOps concepts such as experiment tracking, model deployment, model monitoring, CI/CD, version control, testing, model registry, and retraining.
• Ability to work with noisy, incomplete, high-frequency, or fragmented operational data.
• Ability to communicate technical findings clearly to plant teams, engineers, leaders, and non-technical stakeholders.
• Ability to operate in ambiguous environments where requirements, data quality, and success criteria may need to be clarified.
• Professional confidence to challenge assumptions, push back constructively, and influence stakeholders with evidence.
• Demonstrated ability to learn new technical and business domains quickly

Key Skills

Education

Bachelor's or Master's degrees