Data Science Engineer

Job Summary: We are looking for a Machine Learning Engineer with strong data engineering capabilities to support the development and deployment of predictive models in a smart manufacturing environment. This role involves building robust data pipelines, developing high-accuracy ML models for defect prediction, and implementing automated control systems for real-time corrective actions on the production floor. Key Responsibilities: Data Engineering & Integration: Validate and ensure the correct flow of data from Influx DB/CDL to Smart box/Databricks. Assist data scientists in the initial modeling phase through reliable data provisioning. Provide ongoing support for data pipeline corrections and ad-hoc data extraction. ML Model Development for Defect Prediction: Develop 3 separate ML models for predicting 3 types of defects based on historical data. Predict defect occurrence within a 5-minute window using: Artificial sampling techniques Dimensionality reduction Deliver results with: Accuracy 95% Precision & recall 80% Feature importance insights Closed-Loop Control System Implementation: Prescribe machine setpoint changes based on model outputs to prevent defect occurrence. Design and implement a closed-loop system that includes: Real-time data fetching from production line PLCs (via Influx DB/CDL). Deployment of ML models on Smart box. Pipeline to output recommendations to the appropriate PLC tag. Retraining pipeline triggered by drift detection (cloud-based retraining when recommendations deviate from centerlines). Qualifications: Education: Bachelor's or Masters degree in Computer Science, Data Science, Electrical Engineering, or related field. Technical Skills: Proficient in Python and ML libraries (e.g., scikit-learn, XG Boost, pandas) Experience with: Influx DB and CDL for industrial data integration Smart box and Databricks for model deployment and data processing Real-time data pipelines and industrial control systems (PLCs) Model performance tracking and retraining pipelines Preferred: Experience in manufacturing analytics or predictive maintenance Familiarity with Industry 4.0 principles and edge/cloud hybrid architectures Soft Skills: Strong analytical and problem-solving abilities Effective communication with cross-functional teams (data science, automation, production) Attention to detail and focus on solution reliability