Insight Global are seeking a data scientist who is passionate about the physical world and wants to see their code come to life in the hum of machinery. You will collaborate shoulder-to-shoulder with mechanical, electrical, combustion and controls engineers to move beyond reactive problem-solving and create a proactive, data-driven manufacturing environment. Must have hands on experience building and deploying (into production) Machine Learning models.This position will report to Downtown San Diego 3-5x weekly. The team is looking to bring on a longstanding contractor to their Analytics & AI/ML Team! The contract rate can range from $70-100/hr W2. What You'll Do: A Day in the Life • Build & Deploy Predictive Models: You will develop, train, and deploy machine learning models to predict equipment failures, forecast component lifespan, and identify sources of process instability. This includes everything from data ingestion and feature engineering to model validation and operational deployment. • Pioneer Generative AI Applications: You will research, prototype, and implement Generative AI solutions to accelerate engineering workflows. This includes developing systems for intelligent document search across technical manuals, generating synthetic sensor data to augment our datasets, and creating AI-powered assistants to support root cause analysis. • Master Our Sensor Data: Dive deep into complex, high-velocity datasets from our PLCs and data historians (OSIsoft PI). You will use advanced analytical techniques to clean, transform, and extract meaningful features from vibration, acoustic, temperature, and pressure sensor streams. • Conduct Root Cause Analysis: When a process fails or quality dips, you will lead the analytical investigation. You’ll apply rigorous statistical methods to uncover the "why" behind the problem and present your findings to engineering teams. • Visualize the Factory Floor: Design and build compelling, intuitive dashboards (in Power BI, Tableau, or Grafana) that give our engineers and operators real-time visibility into machine health and process performance. Your visualizations will be the lens through which we see our operations. • Drive Data Strategy: Advise controls and automation engineers on sensor placement, data acquisition rates, and data logging requirements to ensure we are capturing the right data to solve future problems. Mathematical Foundations: • Signal Processing: You aren't afraid of the raw signal. You understand and can apply techniques like Fourier Transforms (FFT) and wavelet analysis to diagnose machinery faults from sensor data. • Statistical Rigor: You have a deep understanding of Statistical Process Control (SPC), Design of Experiments (DOE), and hypothesis testing. You know how to separate signal from noise.